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Home / Behind the Footlights / CHAPTER XVII A GIRL IN THE PROVINCES Why Women go on the Stage—How to prevent it—Miss Florence St. John—Provincial Company—Theatrical Basket—A Fit-up Tour—A Theatre Tour—Répertoire Tour—Strange Landladies—Bills—The Longed-for Joint—Second-hand Clothes—Buying a Part—Why Men Deteriorate—Oceans of Tea—E. S. Willard—Why he Prefers America—A Hunt for Rooms—A Kindly Clergyman—A Drunken Landlady—How the Dog Saved an Awkward Predicament.
CHAPTER XVII A GIRL IN THE PROVINCES Why Women go on the Stage—How to prevent it—Miss Florence St. John—Provincial Company—Theatrical Basket—A Fit-up Tour—A Theatre Tour—Répertoire Tour—Strange Landladies—Bills—The Longed-for Joint—Second-hand Clothes—Buying a Part—Why Men Deteriorate—Oceans of Tea—E. S. Willard—Why he Prefers America—A Hunt for Rooms—A Kindly Clergyman—A Drunken Landlady—How the Dog Saved an Awkward Predicament.
The Project Gutenberg eBook of Behind the Footlights, by Mrs (Ethel) Alec-Tweedie
CHAPTER XVII
A GIRL IN THE PROVINCES
Why Women go on the Stage—How to prevent it—Miss Florence St. John—Provincial Company—Theatrical Basket—A Fit-up Tour—A Theatre Tour—Répertoire Tour—Strange Landladies—Bills—The Longed-for Joint—Second-hand Clothes—Buying a Part—Why Men Deteriorate—Oceans of Tea—E. S. Willard—Why he Prefers America—A Hunt for Rooms—A Kindly Clergyman—A Drunken Landlady—How the Dog Saved an Awkward Predicament.
IT is continually being asked: Why do women crowd the stage?
The answer is a simple one—because men fail to provide for them. If every man, willing and able to maintain a wife, married, there would still be over a million women left. Many women besides these "superfluous" ones will never marry—many husbands will die, and leave their widows penniless, and therefore several millions of women in Great Britain must work to live. Their parents bring them into the world, but they do not always give them the means of livelihood.
Marriage with love is entering a heaven with one's eyes shut, but marriage without love is entering hell with them open.
Women must work until men learn to protect and provide for, not only their wives, but their mothers, daughters, and sisters. All men should respect the woman toiler who prefers work to starvation, as all must deplore the necessity that forces her into such a position. Women of gentle blood are the greatest sufferers; brought up in luxury, they are often thrust on the world to starve through no fault of their own what ever. The middle-class father should also be obliged to make some provision by insurance for every baby girl, which will enable her to live, and give her at least the necessities of life, so that she may not be driven to sell herself to a husband, or die of starvation. The sons can work for themselves, and might have a less expensive up-bringing, so that the daughters may be provided for by insurance, if the tragedies of womanhood now enacted on every side are to cease.
It is no good for young men to shriek at the invasion of the labour market by women: the young men must deny themselves a little and provide for their women folk if it is to be otherwise. It is no good grinding down the wages of women workers, for that does harm to men and women alike, and only benefits the employer. Women must work as things are, and women do work in spite of physical drawbacks, in spite of political handicap, in spite—too often—of lack of sound education. The unfortunate part is that women work for less pay than men, under far harder conditions, and the very men who abuse them for competing on their own ground, are the men who do not raise a hand to make provision for their own women folk, or try in any way to help the present disastrous condition of affairs.
Men can stop this overcrowding of every profession by women if they really try, and until they do so they should cease to resent a state of affairs which they themselves have brought about.
Luckily there is hardly any trade or profession closed to women to-day. They cannot be soldiers, sailors, firemen, policemen, barristers, judges, or clergymen in England, but they can be nearly everything else. Even now, in these so-called enlightened days, men often leave what money they have to their sons and let chance look after their daughters. They leave their daughters four alternatives—to starve, to live on the bitter bread of charity, to marry, or to work. Independent means is a heritage that seldom falls to the lot of women. There are too many women on the stage as there are too many women everywhere else; but on the stage as in authorship, women are at least fairly treated as regards salary, and can earn, and do earn, just as much as men.
The provinces are the school of actors and actresses, so let us now turn to a provincial company, for after all the really hard work of theatrical life is most severely felt in the provinces. A pathetic little account of early struggles appeared lately from the pen of Miss Florence St. John. At fourteen years of age she sang with a Diorama along the South coast, and a few months after she married. Her parents were so angry they would have nothing more to do with her, and not long afterwards her husband's health failed and he died. Sheer want pursued her during those years.
"My efforts to secure work seemed almost hopeless."
That is the crux of so many theatrical lives. Those eight words so often appear—and yet there are sanguine people who imagine employment can always be obtained on the stage for the mere asking, which is not so; but let us now follow the fortunes of a lucky one.
After a play has been sufficiently coached in London, at the last rehearsal a "call" is put up on the board, which says:
"Train call. All artistes are to be at —— Station at —— o'clock on such and such a date. Train arrives at A—— at —— o'clock."
When the actors reach the station they find compartments engaged for them, it being seldom necessary nowadays to charter a private train. Those compartments are labelled in large lettering with the name of the play for which they have been secured. The party travel third class, the manager as a rule reserving first-class compartments for himself and the stars. Generally the others go in twos and twos according to their rank in the theatre, that is to say, the first and second lady travel together, the third and fourth, and so on. Often the men play cards during the whole journey; generally the women knit, read, or enliven the hours of weary travel by making tea and talk!
At each of the stations where the train pauses people look into the carriages in a most unblushing manner, taking a good stare at the theatrical folk, as if they were wild beasts at the Zoo instead of human beings. Sometimes also they make personal and uncomplimentary remarks, such as:
"Well, she ain't pretty a bit," or, "My! don't she look different hoff and hon!"
Each actress has two supplies of luggage, one of which, namely, a "theatrical basket," contains her stage dresses, and the other the personal belongings which she will require at her lodgings. As a rule, ere leaving London she is given two sets of labels to place on her effects, so that the baggage-man may know where to take her trunks and save her all further trouble.
Naturally theatrical folk must travel on Sunday. On a "Fit-Up" tour, when they arrive at the station of the town in which they are to play, each woman collects her own private property, and those who can afford the expense drive off in a cab, while the others—by far the more numerous—deposit it in the "Left Luggage Office." After securing a room, the tired traveller returns to the station and employs a porter to deliver her belongings.
Sometimes a girl experiences great difficulty in finding a suitable temporary abode, for, although in large towns a list of lodgings can be procured, in smaller places no such help is available, and she may have to trudge from street to street to obtain a decent room at a cheap rate. By the time what is wanted is found, she generally feels so weary she is only too thankful to share whatever the landlady may chance to have in the way of food, instead of going out and procuring the same for herself.
On a "Theatre Tour" the members of a company nearly always engage their rooms beforehand and order dinner in advance, because they can go to recognised theatrical lodgings, a list of which may be procured by applying to the Actors' Association, an excellent institution which helps and protects theatrical folk in many ways. When rooms can be arranged beforehand, life becomes easier; but this is not always possible, and then poor wandering mummers meet with disagreeable experiences, such as finding themselves in undesirable lodgings, or at the tender mercy of a landlady who is too fond of intoxicants. A liberal use of insect powder is necessary in smaller towns.
A girl friend who decided to go on the stage has given me some valuable information gathered during six or seven years' experience of provincial theatrical life. Hers are the experiences of the novice, and bear out Mrs. Kendal's advice in an earlier chapter. She was not quite dependent on her profession, having small means, but for which she says she must have starved many a time during her noviciate.
"One comes across various types of landladies," she explained, "but they are nearly always good-natured, otherwise they would never put up with the erratic hours for meals, and the late return of their lodgers. Some of them have been actresses themselves in the olden days, but, having married, they desire to 'lead a respectable life,' by which remark they wish one to understand that the would-be lodger is not considered 'respectable' so long as she remains in the theatrical profession.
"They are sometimes very amusing, at others the reminiscences of their own experiences prove a little trying; but after all, even such folk are better than the type of lodging-house-keeper who has come down in the world, and is always referring to her 'better days.' A great many of these people do not appear ever to have had better days. Now and then, however, one finds a genuine case and receives every possible attention, being made happy with flowers—a real luxury when on tour—nice table linen, fresh towels, all things done in a civilised manner, and oh dear! what a joy it is to come across such a home."
"Are the rooms, then, generally very bare?" I asked.
"One never finds any luxuries. As a rule one has to be content with horsehair-covered chairs and sofas, woollen antimacassars, wax or bead flowers under glass cases, often with the addition of a stuffed parrot brought home by some favourite sailor son. But simplicity does not matter at all so long as the lodgings do not smell stuffy. The bedroom furniture generally consists of the barest necessaries, and if one's couch have springs or a soft mattress it proves indeed a delightful surprise.
"There is a terrible type of landlady who rushes one for a large bill just at the last moment. As a rule the account should be brought up on Saturday night and settled, but this sort of woman generally manages to put off producing hers until the last moment on Sunday morning, when one's luggage is probably on its way to the station. Then she brings forth a document which takes all the joy out of life, and sends the unhappy lodger off without a penny in her pocket. Arguing is not of the slightest use, and if one happens to be a woman, as in my case, she has to pay what is demanded rather than risk a scene."
My friend's experiences were so practical I asked her many questions, in reply to some of which she continued:
"I have always managed to share expenses with some one I knew, which arrangement, besides being less lonely, reduced the cost considerably; but even then there is a terrible sameness about one's food. An egg for breakfast is very general, as some 'ladies' even object to cooking a rasher of bacon. Jam and other delicacies are beyond our means. Everlasting chop or steak with potatoes for dinner. One never sees a joint; it is not possible unless a slice can be begged from the landlady, in which case one often has to pay dearly for the luxury.
"We generally have supper after we return from the theatre, from which we often have to walk home a mile or more after changing. Many landladies refuse to cook anything hot at night, in which case tinned tongue or potted meat suffice; but a hot meal, though consisting only of a little piece of fish or poached eggs, is such a joy when one comes home tired and worn out, that it is worth a struggle to try to obtain.
"The least a bill ever comes to in a week is fifteen shillings, and that after studying economy in every way possible. Even though two of us lived together I never succeeded in reducing my share below that."
"What is the usual day?"
"One has breakfast as a rule between ten and eleven—earlier, of course, if a rehearsal has been called for eleven, in which case ten minutes' grace is given for the difference in local clocks; any one late after that time gets sharply reprimanded by the management. After rehearsal on tour a walk till two or three, a little shopping, dinner 4.30, a rest, a cup of tea at 6.30, after which meal one again proceeds to the theatre, home about 11.30, supper and bed. Week in, week out it is pretty much the same.
"For the first four years I only earned a guinea a week, and as it was necessary for me to find all my own costumes for the different parts in the companies in which I played, I had to visit second-hand shops and buy ladies' cast-off ball dresses and things of that sort, although cheap materials and my sewing machine managed to supply me with day garments. It is extraordinary what wonderful effects one can get over the footlights with a dress which by daylight looks absolutely filthy and tawdry, provided it be well cut; that is why it is advisable to buy good second-hand clothes when possible.
"In my own theatre basket I have fourteen complete costumes, and with these I can go on any ordinary tour. I travelled for some time with a girl who, though well-born, had out of her miserable guinea a week to help members of her family at home. She was an excellent needlewoman, and used to send her sewing-machine with her basket to the theatre, where she sat nearly all day making clothes or cutting them out for other members of the company. By these means she earned a few extra shillings a week, which helped towards the expenses of her kinsfolk. She was a nice girl, but delicate, and I always felt she ought to have had all the fresh air possible instead of bending over a sewing-machine in a stuffy little dressing-room.
"Of course it is necessary for us to take great care of our private clothes, and in order to save them I generally keep an old skirt for trudging backwards and forwards through the dust and dirt, and for rehearsals, since at some of the ill-kept provincial theatres a good gown would be ruined in a few days; added to which, one often gets soaked on the way to and from the theatre, for we can rarely afford cabs, and even if we could, on a wet night the audience take all available vehicles, so that by the time the performers are ready to leave, not one is to be procured."
Perhaps it may be well to say a little more concerning the theatre basket. It looks like a large washing basket, but being made of wicker-work is light. It is lined inside with mackintosh, and bears the name of the company to which it belongs on the outside. It is taken to the theatre on Sunday when the party arrives in the town, and as a rule each actress goes first thing on Monday morning for rehearsal and to unpack. The ordinary provincial company usually comprises about five men and five women, but in important dramas there are many more, and sometimes a dozen women and girls will have to dress in one room.
Of course the principal actresses select the best dressing-rooms, and each chooses according to her rank. Round the wall of the room a table is fastened, such a table as one might find in a dairy, under which the dress baskets stand. Those who can afford it, provide their own looking-glass and toilet-cover to put over their scrap of table, also sheets to cover the dirty walls, ere hanging up their skirts; but as every one cannot afford to pay for the washing of such luxuries, many have to dispense with them.
There is seldom a green-room in the provinces, so as a rule the actresses sit upon their own baskets during the waits; and as in many theatres there are no fireplaces in these little dressing-rooms, and not always artificial heat, there they remain huddled in shawls waiting their "call."
"The most interesting form of company," said my friend, "is the 'Répertoire,' for that will probably give three different pieces a week, which is much more lively than performing in the same play every night for months.
From a painting by Hugh de T. Glazebrook.
MRS. PATRICK CAMPBELL.
"If any one falls out of the cast through illness or any other reason, and a new man or woman join the company, a fortnight is required for rehearsals, and during that fortnight we unfortunate players have to give our gratuitous services every day for some hours."
On asking her whether she thought it wise for a girl to choose the stage as a profession, she shook her head sadly.
"I do not think a woman should ever choose the stage as a profession if she have any person depending upon her, for it is practically impossible to live on one's precarious earnings. It is only the lucky few who can ever hope to make a regular income, and certainly in the provinces very few of us do even that. Many managers like to engage husbands and wives for their company, as this means a joint salary and a saving in consequence. These married couples do not generally get on well, and certainly fail to impress one with the bliss of professional wedded life."
"What are the chances of success?" I inquired.
"The chances of getting on at all on the stage are small in these days, when advancement means one must either have influence at headquarters, or be able to bring grist to the manager's mill. It is heart-breaking for those who feel they could succeed if they were but given a chance, to see less talented but more influential sisters pushed into positions. One gradually loses all hope of true merit finding its own reward, while it is no uncommon thing for a girl to pay down £20 to be allowed to play a certain part. She may be utterly unfitted for the rôle, but £20 is not to be scoffed at, and she is therefore pitchforked into it to succeed or fail. In most cases she fails, and cannot get another engagement unless she produces a second £20.
"No, I do not consider the stage a good profession for a girl, simply because there is no authority over her, and few people take enough interest in the young creature to even warn her of the peril. In the theatrical profession, and especially on tour, the sexes meet on an equal footing. No chivalry need be expected, and is certainly rarely received, because when one is vouchsafed any little attention or politeness, such as one would naturally claim in society or take for granted in daily intercourse, it is merely because the man has some natural instinct which causes him to be polite in spite of adverse circumstances.
"The majority of men upon the stage to-day are so-called gentlemen, but there is something in the life which does not conduce to keep them up to the standard from which they start. They become careless in their manners, dress, and conversation, and keep their best side for the audience. As a rule they are kind-hearted and willing to help women, but men upon the stage get 'petty.' I do not know whether it is the effect of the paint, the powder, and the clothes, or the fact of their doing nothing all day, but they certainly deteriorate; one sees the decadence month by month. They begin by being keen on sport, for instance, but gradually they find even moving their bicycles about an expense and leave them behind. They have nowhere to go, are not even temporary members of clubs, so gradually get into the habit of staying in bed till twelve or even two o'clock for lack of something to interest them, and finish the rest of the day in a 'gin crawl,' which simply means sitting in public-houses drinking and smoking.
"Unfortunately this love of drink sometimes increases, and as alcohol can be readily procured by the dresser, men and women too, feeling exhausted, often take things which had better be avoided. You see their meals are not sufficiently substantial—how can they be on the salary paid? Girls live on small rations of bread, butter, and oceans of tea, and the men on endless sausage rolls and mugs of beer."
This reminds me of a little chat I had with E. S. Willard. On the fiftieth night of that excellent play The Cardinal, by Louis N. Parker, at the St. James's Theatre, a mutual friend came to ask me to pay a visit behind the stage to the great Mr. Willard.
We arrived in Mr. Alexander's sitting-room described elsewhere, at the end of the third act, and a moment later the rustling silk of the Cardinal's robe was heard in the passage.
"I'm afraid this is unkind of me," I said: "after that great scene you deserve a 'whisky and soda' instead of a woman and talk."
"Not at all," said this splendid-looking ecclesiastic, seating himself gaily. "I never take anything of that sort till my work is done."
"But you must be fearfully exhausted after such a big scene?"
"No. It is the eighth performance this week, and the second to-day; but I'm not really tired, and love my work, although I do enjoy my Sunday's rest."
Mr. Willard looks handsomer off the stage than on. His strong face seems to have a kindlier smile, his manner to be even more courtly, and I was particularly struck with the fact that he wore little or no make-up.
"You are an Englishman," I said, "and yet you have deserted your native land for America?"
"Not so. I'm English, of course, though I love America," was the reply. "Seven years ago I went across the Atlantic and was successful, then I had a terrible illness which lasted three years. When I was better I did not dare start afresh in England and risk failure, so I began again in the States, where I was sure of the dollars. They have been so kind to me over there that I do not now like to leave them. You see America is so enormous, the constant influx of emigrants so great, one can go on playing the same piece for years and years, as Jefferson is still doing in Rip van Winkle. Here new plays are constantly wanted, and even if an actor is an old favourite he cannot drag a poor play to success. Management in London has become a risky matter. Expenses are enormous, and a few failures mean ruin."
Alas! at that moment the wretched little bell which heralds a new act rang forth, and I barely had time to reach the box before Mr. Willard was once more upon the stage, continuing his masterly performance. He is an actor of strong personality, and can ill be spared from England's shores.
But to return to the provinces, and the experiences of the pretty little actress.
"The familiarity which necessarily exists between the sexes," continued she, "both in acting together at night, and rehearsing together by day, is in itself a danger to some girls who are unfortunate enough to be thrown into close companionship with unprincipled men, and have not sufficient worldly wisdom or instinct to guard against their advances.
"The idea of the stage door being besieged by admirers is far from true in the provinces. With musical comedies of rather a low order there may be a certain amount of hanging about after the performance, but in the case of an ordinary company this rarely happens. The real danger in the provinces does not come from outside.
"Life on tour for a single man is anything but agreeable. He has no one to look after his clothes, for, needless to say, no landlady will do that, and therefore both his theatre outfit and his private garments are always getting torn and worn. As a rule, however, there are capable women in the company who are willing to sew on buttons, mend, or darn, and if it were not for their good nature, many men would find themselves in sorry plight."
She was an intelligent, clever girl, and I asked her how she got on the stage.
"After having been trained under a well-known manager for six months and paying him thirty guineas for his services, I was offered an engagement in one of his companies then starting for a 'Fit-Up' tour through Scotland at a £1 week, payable in two instalments, namely, 10s. on Wednesday and 10s. on Saturday. Fortunately, being a costume play, dresses were provided, but I had to buy tights, grease-paint, sandals, and various ornaments, give two weeks' rehearsals in London free, play for three nights and live for three days in Scotland before I received even the first ten shillings.
"Happily I was the proud possessor of small means, and shared my rooms and everything with a girl friend who had trained at the same time as myself, consequently we managed with great care to make both ends meet; but it was hard work for us even with my little extra money, and what girls do who have to live entirely on their pay, and put by something for the time when they are out of an engagement, a time which often comes, I do not pretend to know.
"A 'Fit-Up' tour is admittedly the most expensive kind of work for actors, because it means that three nights is the longest period one ever remains in any town, most of the time being booked for 'one-night places' only. On this particular tour of sixteen weeks there were no less than sixty 'one-night places,' and my total salary amounted to £16.
"It may sound ridiculous to travel with a dog, but mine proved of the greatest use to me on more than one occasion. Our first hunt was always for rooms; the term sounds grand, for the 'rooms' generally consisted of one chamber with a bed sunk into the wall, as they are to-day at a great public school like Harrow. To get to this abode we sometimes had to pass through the family apartments, a most embarrassing proceeding, as the members had generally retired to rest before our return from the theatre; but still, 'beggars cannot be choosers,' and in some ways we often felt ourselves in that position.
"Supposing we arrived at a one-night place, we would sally forth and buy
lb. tea,
lb. butter,
small loaf,
lb. steak or chop for dinner,
eggs for breakfast.
"The landlady's charge as a rule for two lodgers sharing expenses varied from 2s. 6d. to 3s. for a single night, or 5s. for three nights, so that the one-night business was terribly extravagant.
"Being our first tour we were greatly interested by the novelty of everything; it was this novelty and excitement which carried us through. We really needed to be sharp and quick, for in that particular play we had to change our apparel no less than six times. We were Roman ladies, slaves, and Christians intermittently during the evening, being among those massacred in the second act, and resuscitated to be eaten by lions at the end of the play; therefore, while the audience were moved to tears picturing us being devoured by roaring beasts, we were ourselves roaring in the wings in imitation of those bloodthirsty animals.
"A 'Fit-Up' carries all its own scenery, and nearly always goes to small towns which have no theatre, only a Town Hall or Corn Exchange, while the dressing-rooms, especially in the latter, are often extremely funny, being like little stalls in a stable, where we sometimes found corn on the floor, and could look over at each other like horses in their stalls.
"The 'Fit-Up' takes its own carpenter, who generally plays two or three parts during the evening. He has to make the stage fit the scenery or vice versâ, and get everything into working order for the evening performance.
"On one occasion we arrived at a little town in Scotland and started off on our usual hunt for rooms. We were growing tired and depressed; time was creeping on, and if we did not obtain a meal and rooms soon, we knew we should have to go to the theatre hungry, and spend that night in the wings. Matters were really getting desperate when we met two other members of the company in similar plight. One of them was boldly courageous, however, and when we saw a clergyman coming towards us, suggested she should ask him if he knew of any likely place. She did so, and he very kindly told her to mention his name at an inn where he was sure they would, if possible, put her and her friend up, but he added, 'There is only one room.' This, of course, did not help my friend and myself, so after the two had started off we stood wondering what was to become of us.
"'Can you not tell us of any other place?' we asked. No, he could not, but at this moment a lady appeared on the scene who asked what we wanted. We explained the difficulty of our situation, and she pondered and thought, but intimated there was no lodging she could recommend, whereupon we proceeded disconsolately on our way, not in the least knowing what we were to do.
"A moment or two afterwards we heard some one running behind. It was the clergyman. Taking off his hat and almost breathless, he exclaimed, 'My wife wishes to speak to you,' and lo and behold that dear wife hurried after him to say she felt so sorry for the position in which we were placed that she would be very glad if my friend and I would give her the pleasure of our company and stay at her house for the night.
"We went. She sent from the vicarage to the station for our belongings, and we could not have been more kindly treated if we had been her dearest friends. She had a fire lighted in our bedroom, and there were lovely flowers on the table when we returned from the theatre. They took us for a charming expedition to some old ruins on the following morning, invited friends to meet us at luncheon, and although they did not go to the theatre themselves at night, they sat up for us and had a delightful little supper prepared against our return.
"I shall never forget the great kindness they showed us. I am sure there are very few people who would be tempted to proffer such courtesy and hospitality to two wandering actresses; and yet if they only knew how warmly their goodness was appreciated and how beneficent its influence proved, they would feel well repaid.
"In the afternoon when it was time to leave, rain was pouring down, but that fact did not deter the clergyman from accompanying us to the station, carrying an umbrella in one hand and a bag in the other, while his little son followed with a great bunch of flowers.
"As if to take us down after such luxurious quarters, we fell upon evil days at the very next town, where we were told it was difficult to get accommodation at all, and therefore made up our minds to take the first we met. It did not look inviting, but the woman said that by the time we had done our shopping she would have everything clean and straight. We bought our little necessaries, and as the door was opened by a small boy handed them in to him, saying we were going for a walk but would be back in less than an hour for tea. On our return we were admitted, but saw no signs of tea, so rang the bell. No one came. We waited ten minutes and rang again. A pause. Suddenly the door was burst open and in reeled the landlady, who banged down a jug of boiling water on the table and departed. We gazed at each other in utter consternation, feeling very much frightened, for we both realised she was drunk.
"We rang again after a time, but as no one attempted to answer our summons, and it being impossible to make a meal off hot water, I crept forth to reconnoitre. There was not a soul to be seen, not even the little boy, but I ventured into the kitchen to try if I could not find the bread, butter, and tea, so that we might prepare something to eat for ourselves. While so engaged a sonorous sound made me turn round, and there upon the floor with her head resting upon a chair in the corner of the room lay our landlady, dead drunk. It was an appalling sight. We gathered our things together as quickly as we could and determined to leave, put a shilling on the table to appease the good woman's wrath when she awoke, and were glad to shake the dust of her home from our feet.
"Not far off was a Temperance Hotel, the sight of which after our recent experience we hailed with delight, and where we engaged a bedroom, to which we repaired, when our evening's work was finished.
"My dog, who always lay at the foot of my bed, woke us in the middle of the night by his low growls. He seemed much perturbed, so we lay and listened. The cause of his anxiety soon became clear; some one was trying to turn the handle of the door, while the voices of two men could be heard distinctly, one of which said:
"'Only two actresses, go on,' and then the door handle turned again and his friend was pushed in. It was all dark, but at that moment my dog's growls and barks became so furious and angry as he sprang from the bed that the man precipitately departed, and we were left in peace, although too nervous to sleep.
"Of course we complained next morning, but equally of course the landlady knew nothing about the matter. These were our best and worst experiences during my first tour."

redpajama_set_name: RedPajamaCommonCrawl


text:
\section{Introduction}
\IEEEPARstart{T}{he} recognition of human actions is an active research field in recent years and much effort has been made to address this problem \cite{action_survey08}. Intuitively, a temporal sequence of 3D skeleton joint locations captures sufficient information to distinguish between actions, but recording skeleton sequence was very expensive with the traditional motion capture technology,  which limits the applications to which it has been applied \cite{MoCap_review06}. Recently, with the advent of RGB-D cameras such as Microsoft Kinect \cite{Kinect_intro13}, the acquisition of 3D skeleton data for action recognition has become much easier and faster \cite{Realtime_kinect11}. This advance promotes a number of skeleton-based action recognition approaches \cite{LieGroup14, HOD13, DBM_HMM14}. The key challenge of these approaches is how to extract discriminative features from the noisy temporally evolving skeletons.

\begin{figure}[t]
\begin{center}

   \includegraphics[width=1.0\linewidth]{./fig/action_compare.png}
\end{center}
   \caption{Skeleton sequences from two action classes.
   Only the red skeletons show significant differences between the two sequences. In this example, less than $20\%$ of the frames are required to tell whether the skeleton is clapping or waving.}
\label{fig: ex_temp}
\end{figure}

The trajectory of skeletal joints in space-time is the direct representation of human actions.
 Earlier works \cite{traj_intro08, traj_intro10} model human action trajectory descriptors of variable-lengths and classify them based on the similarity matching of trajectories. In \cite{HOD13}, an action representation is encoded with a histogram voted by the displacements of joint trajectories with respect to their orientations. In these works, the global feature is extracted from the whole trajectory. However, only a short section of the trajectory is actually distinctive and can provide usable information about the action being undertaken. For example, as illustrated in Figure~\ref{fig: ex_temp}, only moments when the subject moves its hands,  during the two actions of waving and clapping, are indicative of the performed action class, while all the remaining poses are irrelevant and potentially distracting. The abundant non-informative local patterns may cause large variance to the global trajectory. Compared to the global representation, later works \cite{eigenjoints12, MovingPose} explore discriminative patterns to create local descriptors at frame-level. Despite its robustness, a frame-level descriptor, without additional temporal information, hardly depicts the movement of actions, and is insufficient for recognition.

Different from the above-mentioned approaches which either represent an action with the whole sequence or extract local features at the frame level, we argue that the discriminative information of an action is better captured by a short interval of trajectories. This interval usually consists of several frames. In other words, its temporal range is longer than a single frame but much shorter than the whole skeleton sequence. To extract features from the trajectory interval, we make our first contribution by designing a novel local descriptor called trajectorylet to capture the static and dynamic motion information within the short interval.

 Furthermore, as we have observed, not all trajectorylets in a sequence are equally important for classification and the recognition performance generally benefits from focusing on the discriminative ones.
In skeleton-based action recognition, recent works \cite{MovingPose, Canonical13} directly learn the discriminative frames from the training set.  Unlike the aforementioned works, our approach does not explicitly look for the discriminative trajectorylets, but rather provides a method for creating a set of detectors that fire on specific template trajectorylets. Our approach firstly applies exemplar-SVM \cite{ESVM11} to learn a large number of candidate detectors and then selects detectors according to their discriminative performance over the trajectorylets in the training set. We further cluster detectors into multiple clusters, and remove the redundancy of the learned detectors by selecting one representative detector from each cluster. The selected detectors form a template detector set and their detection scores on a trajectorylet is utilized as the coding vector of that trajectorylet. The action level representation is then obtained by pooling all trajectorylet coding vectors and temporal pyramid pooling can also be incorporated to capture the long range temporal information of the action sequence. In extensive experiments, this framework brings significant performance improvement over state-of-the-art approaches for skeleton-based action recognition.

In summary,
our {\em first contribution}
is the trajectorylet, a novel local descriptor that captures static and dynamic information
in a short interval of joint trajectories. In our {\em second contribution},
a novel framework is proposed to generate robust and discriminative representation for action instances from a set of learned template trajectorylet detectors.

Following  briefly reviewing related literature in Section~\ref{sec: rel_work},
we propose the design of our local feature and detector learning method in Section~\ref{sec: FL}.
We then present the action-level representation of an action instance in Section~\ref{sec: GL}.
Our framework is experimentally evaluated in Section~\ref{sec: EX} and summarized in Section~\ref{sec: CONC}.

\section{Related work}
\label{sec: rel_work}
The key challenge of skeleton-based action recognition is how to construct the action representation from a sequence of skeletal joints. Some video-based methods \cite{Track_points09, dense_traj11} extract trajectories of multiple tracking points, and compute descriptors along them, such as HOG, HOF and MBH. For skeleton-based methods, trajectories are directly obtained from the space-time evolution of skeletal joints. The most straightforward way is to model the trajectory holistically, either by extracting statistics from the sequence or modelling its generative process. In \cite{HOD13}, a histogram records the displacements of joint orientations over the whole trajectory. In \cite{jointangles13}, the action is modelled with the pairwise affinities trajectories of joint angles. In \cite{HOJ3D13}, the action sequence is modelled by the Hidden Markov Model with quantized histogram of spherical coordinates of joint locations as frame-level feature. In \cite{LieGroup14}, 3D geometric relationships between various body parts are modelled with a Lie group to represent the whole action.

Besides directly modelling the trajectory holistically, it has also been noted that only a small fraction of patterns of a skeletal sequence are actually distinctive and thus many approaches have been proposed to identify those discriminative patterns, whether these patterns are defined spatially or temporally.

It has been found that not all skeletal joints are informative for distinguishing one action from the others, therefore it is beneficial to select a subset of joints. Ofli \mbox{\emph{et al.\ }} \cite{SMIJ12} select a subset of most informative joints according to criteria such as mean or variance of joint angles. In \cite{Actionlet12},  joints are grouped into actionlets, and the most discriminative collection of them are mined via the multiple kernel learning approach. In \cite{bio13}, a subset of joints within a short-time interval is extracted according to the spatio-temporal hierarchy of the moving skeleton, and a linear combination of them is learned via a discriminative metric learning approach. In \cite{Pose_based13}, the distinctive set of body parts are mined from their co-occurring spatial and temporal  configurations. In \cite{Evo14}, an evolution algorithm is employed to select an optimal subset of joints for action representation and classification is performed by using DTW-based sequence matching.

As most of the frames in an action sequence are comprised of non-distinctive static poses, features at a few discriminative temporal locations are informative enough to represent an action. In video-based action recognition, a number of key frame selection approaches have been proposed. In \cite{infoframe08}, key frames are selected by ranking the conditional entropy of the codewords assigned to the frames. In \cite{Poseltekeyframe13}, the locations of key frames are modelled as latent variables and estimated for each action instance by dynamic programming. In recent works on skeleton-based action recognition, distinctive canonical poses \cite{Canonical13} are learned via logistic regression, and discriminative frames \cite{MovingPose} are identified by their approximated confidence of belonging to a specific action class. In \cite{eigenjoints12}, distinctiveness of each frame is calculated by a measurement of accumulated motion energy.

\section{The proposed action representation}
\label{sec: FL}
Our model utilizes the relationships between the positions of the $J$ skeletal joints ${\mathbf j}_j=(x_j, y_j, z_j) \in \mathbb{R}^3, j=1 \cdots J$ in the current and preceding frames to form a local trajectorylet. Because human skeleton size varies from different action instances, we perform a skeleton size normalization on the raw skeletal joints according to \cite{MovingPose}. We also subtract the position of the hip center ${\mathbf j}_{hip}$ from each joint and concatenate them to form a feature column: ${\mathbf j}=[{\mathbf j}_1 - {\mathbf j}_{hip}, \cdots, {\mathbf j}_J - {\mathbf j}_{hip}] \in \mathbb{R}^{3J}$, making ${\mathbf j}_{hip}$ the origin point of the coordinate system across all frames and subjects.

\subsection{Trajectorylet}
Although holistic trajectories of joints depict the movement of human body, distinctive patterns are usually overwhelmed by common ones. For example, in long-term actions such as draw circle and draw tick, only the last moment of drawing movement distinguishes them, before which both trajectories share the same movement of raising up hand for a long time. On the other hand, as depicted in Figure~\ref{fig: visual_traj},  frame-level local descriptors record current poses and some local dynamics, but they fail to capture the movement that spans a long temporal range. To distinguish walk from run, for instance, we need to examine the displacement and speed of the joints within a sufficient period of time, rather than the static poses. Based on these observations, we propose our trajectorylet local descriptor, which captures the static and dynamic information of trajectories in a short period of time. Compared with frame-level descriptors, trajectorylet depicts richer dynamic information. On the other hand, its temporal range is much smaller than the whole trajectory sequence and therefore it is less affected by potentially irrelevant frames.

\begin{figure}[t]
\begin{center}

   \includegraphics[width=0.6\linewidth]{./fig/visual_traj.png}
\end{center}
   \caption{The joint coordinate information at frame-level may provide little information to distinguish between some action classes, such as the above drawing actions. One of the advantages of trajectorylets is their ability to focus on the dynamics of distinctive sections of individual actions.}
	\label{fig: visual_traj}
\end{figure}

More specifically, considering a trajectorylet of length $L$ starting from frame $t_0$, we extract the static positions of the joints from each frame occurring before time $t_0 + L$:

\begin{equation}
            \label{EQ:curent_frame}
     {\bf x } _{0}^{t_0} = [ {{\mathbf j}^{t_0}}^{\!\top}, {{\mathbf j}^{t_0+1}}^{\!\top}, \cdots, {{\mathbf j}^{t_0+L-1}}^{\!\top} ]^{\!\top} \in  \mathbb{R}^{(L \times 3J)}.
        \end{equation}

In order to retrieve the dynamic information within this interval, we inspect multiple levels of temporal dynamics such as displacement and velocity.

\begin{align}
            \label{EQ:adjacent_frame1}
         &  {\bf x } _{1}^{t_0} = [ {\Delta{\mathbf j}^{t_0+1}}^{\!\top}, \cdots, {\Delta{\mathbf j}^{t_0+L-1}}^{\!\top} ]^{\!\top} \in  \mathbb{R}^{((L-1) \times 3J)}, \\ \notag
         & \Delta{\mathbf j}^{t_0+i}= {\mathbf j}^{t_0+i} -{\mathbf j}^{t_0} ,      \, i=1, \cdots, L-1. \\
         \label{EQ:adjacent_frame2}
         &  {\bf x } _{2}^{t_0} = [ {\Delta^2{\mathbf j}^{t_0+2}}^{\!\top}, \cdots, {\Delta^2{\mathbf j}^{t_0+L-1}}^{\!\top} ]^{\!\top} \in  \mathbb{R}^{((L-2) \times 3J)}, \\
       & \Delta^2{\mathbf j}^{t_0+i}=\Delta{\mathbf j}^{t_0+i} - \Delta{\mathbf j}^{t_0+i-1}, \, i=2, \cdots, L-1. \notag
\end{align}
where $\Delta{\mathbf j}^{t_0+i}$ indicates the relative joint displacements of frame $t_0+i$ from the first frame;  $\Delta^2{\mathbf j}^{t_0+i}$ indicates the joint velocities of frame $t_0+i$ from its previous frame within the trajectorylet. The static positions of ${ {\bf x } _{0}^{t}}$ store the absolute spatial location of the trajectorylet. The temporal dynamics ${ {\bf x } _{1}^{t}}$ and ${ {\bf x } _{2}^{t}}$ approximate the relatively kinematic evolution within this short time interval. Combining both static and dynamic information we define the $t$-th trajectorylet for an action instance with $F$ frames as

\begin{align}
            \label{EQ:def_traj}
     {\bf x } ^{(t)} = ({ {\bf x } _{0}^{t}}^{\!\top},  { {\bf x } _{1}^{t}}^{\!\top}, { {\bf x } _{2}^{t}}^{\!\top})^{{\!\top}} \in {\mathbb{R}}^{(3L-3)3J}.
\end{align}
where $t=1, \cdots, F-L$.

PCA is applied on trajectorylets to reduce the their dimension for our detector learning module. We still denote the final descriptor as $ {\bf x } ^{(t)} \in {\mathbb{R}}^{d}, \, d \leq (3L-3)3J$. Figure \ref{fig: ex_traj} visualizes components in a trajectorylet , including one static component and two dynamic components.

\begin{figure}[t]
\begin{center}
\begin{tabular}{c}
   \includegraphics[width=0.9\linewidth]{./fig/ex_traj.png} \\
   \includegraphics[width=0.9\linewidth]{./fig/ex_traj2.png}
\end{tabular}
\end{center}
   \caption{Visualization of trajetorylet of length 5 at a single joint (left hand). The red point is the position at the starting frame, and the green points are its positions at succeeding frames in this interval. The yellow segments are joint displacements  from the first frame. The black segments are joint velocities at each frame. The top trajetorylet is part of {\it drawing circle} and the bottom trajetorylet is part of {\it high waving}. The differences between them are clearly distinguished by their positions, displacements and velocities over a short period of time.}
	\label{fig: ex_traj}
\end{figure}

\subsection{Learning candidate detectors of discriminative trajetorylet using ESVM}
As we have previously discussed, only a small fraction of the trajectorylets from an sequence contains sufficient information for identifying the associated action. Most of the trajectorylets, especially those that contain the static posture, are shared by multiple action classes. Our aim is to learn a set of detectors that fire on the distinctive trajectorylets. To this end, we firstly resort to exemplar-SVM (ESVM) \cite{ESVM11} to learn a large set of detectors for a large number of sampled trajectorylets, one for each sampled trajectorylet. Then for each action instance we select a few discriminative trajectorylet detectors as the candidate detectors of discriminative trajectorylet.

An ESVM learns a decision boundary that achieves largest margin between an exemplar sample and a set of negative examples.  If we take each trajectorylet as a positive exemplar $ {\bf x } _E$ of its associated class $c$, $c=1, \cdots C$, and trajectorylets that belong to other action classes as the negative examples, we can train an exemplar-SVM for it and formally this can be formulated as:

 %
  %
 \begin{align}
            \label{EQ:ESVM}
	\arg \min_{ {\bf w } _E,b_E}	|| {\bf w } _E||^2 + \lambda_1 h(  {\bf w } _E^{\!\top}  {\bf x } _E + b_E)  \\ \notag
	+ \lambda_2 \sum_{ {\bf x }  \in {\cal N}_c} h(- {\bf w } _E^{\!\top}  {\bf x }  - b_E)
   \end{align}

where $h(x)=\max(0, 1-x)$ is the hinge loss function, and ${\cal N}_c$ is the negative set of trajectorylets that do not belong to class $c$. ${\lambda_1}$ and ${\lambda_2}$ denote the weights of loss for positive and negative samples respectively, and ${\lambda_1} > {\lambda_2}$ ensures that a greater penalty will be applied to the incorrectly classified positive exemplars.

For each ESVM, the trained detector $f( {\bf x } )= {\bf w } _E^{\!\top}  {\bf x }  + b_E$ returns higher scores on trajectorylets that are most similar to $ {\bf x } _E$.
If the current exemplar trajectorylet is common in multiple action classes, the returned trajectorylets are abundant in multiple classes. On the contrary, if the current exemplar trajectorylet is unique for a single class, most returned trajectorylets belong to the same class with the current exemplar trajectorylet. Thus we can employ the distribution of action classes of the returned trajectorylets to estimate the discriminative power of one detector.

\begin{figure*}[ht]

\includegraphics[width=1.0\linewidth]{./fig/learning_overview.png}
\centering
   \caption{Overview of our feature learning framework.}
  %
\label{fig:short}
\end{figure*}

Given an action instance $A$, we extract $F_A$ trajectorylet descriptors $ {\bf x } ^{(t)}~ ,~ t=1, \cdots F_A$, and train the associated detectors $( {\bf w } _E^{(t)}, b_E^{(t)})~,~ t=1, \cdots F_A$. A selection method is implemented to find the most discriminative trajectorylet detector among the candidates. More specifically, we apply each detector $( {\bf w } _E^{(t)}, b_E^{(t)})$ to the trajectorylets $ {\bf x } ^{(i)}$, $i=1, \cdots N$ sampled from the \textit{whole training set} and compute the detection scores $r_{ti}= {\bf w } _E^{(t){\!\top}}  {\bf x } ^{(i)} + b_E^{(t)}$ \footnote{In order to measure the scores on the same scale, we adjust the trained parameters with unit norm before computing the scores.}. From ${\mathcal R}_{t}=\{r_{ti}\}_{i=1, \cdots N}$ we choose a subset ${\mathcal R}'_t$ , with the top $N_A$ scores, corresponding to the trajectorylets that are most compatible with current detector $( {\bf w } _E^{(t)}, b_E^{(t)})$. For the $N_A$ trajectorylets detected by $( {\bf w } _E^{(t)}, b_E^{(t)})$, we denote $h^{(c)}_t$ as the number of trajectorylets belonging to action class $c$ . The histogram $H_t=[h^{(1)}_t, \cdots, h^{(C)}_t]^{{\!\top}} \in \mathbb{R}^C$ gives a clear view of the distinctiveness of detector $( {\bf w } _E^{(t)}, b_E^{(t)})$.

 %

If $H_t$ is flat across many classes, $ {\bf x } ^{(t)}$ is a common pattern shared by many classes and its detector is therefore not distinctive. If the $H_t$ is centered mostly at the correct class, trajectorylet $ {\bf x } ^{(t)}$ is a distinctive pattern for this class and hence $( {\bf w } _E^{(t)}, b_E^{(t)})$ is an effective detector of this distinctive pattern.
In practice, if the correct class corresponding to $( {\bf w } _E^{(t)}, b_E^{(t)})$ is $c$, we denote $P_t=h^{(c)}_t/N_A$ as the ratio of correctly detected trajectorylets and a detector with higher $P_t$ is selected because it fires primarily on trajectorylets with the same class of it, verifying the distinctiveness of this detector. We summarize this approach in Algorithm \ref{ALG:1}.
\input{alg1.tex}

\subsection{Template detector set}

\label{subsec:Cluster}
As the detectors are discovered from every action instance, the size of the detector set grows with the number of training instances, which will lead to a very high-dimensional action representation and make the computation intractable. On the other hand, the above method might select similar distinctive detectors multiple times, resulting in a highly redundant detector set. To control the size of detector set and remove the redundancy of candidate detector set, we perform spectral clustering on candidate detectors and then select one detector from each cluster as the final detector set used for trajectorylet encoding. To build the affinity graph for spectral clustering, we need to specify the similarity measurement between two detectors. Here we measure this similarity by considering the ``active detection scores'' of two detectors which refer to the detection scores with positive values. We evaluate it by firstly calculating detection scores on $N$ sampled trajectorylets and setting negative detection scores to zero. This process gives a $N$ dimensional active detection score vector ${\mathbf r}_{d}$ for each detector and the similarity between two detectors are measured as follows:
 \begin{equation}
            \label{EQ:Sim_Mat}
		\sim_{dd'}= \frac{{\mathbf r}_{d}^{\!\top} {\mathbf r}_{d'}}{||{\mathbf r}_d|| \cdot ||{\mathbf r}_{d'}||}
        \end{equation}
where $\|\cdot\|$ represents the $l^2$ norm, and ${\mathbf r}_{d}$ and ${\mathbf r}_{d'}'$ denote the active detection score vectors for the two compared detectors. The value $\sim_{dd'}$ measures the similarity between two detectors and is used to build the affinity matrix $\mathbf Q$ for the detector set $\mathcal{D}$, that is,  $\mathbf Q = [\sim_{dd'}]_{d,d'=1,\ldots,D}$. We apply spectral clustering to $\mathbf Q$ and obtain $K<D$ clusters of detectors. The detectors within the same cluster fire on similar trajectorylets. From each cluster, we select a representative detector that produces the highest score on the sampled trajectorylets. In practice, given a sufficient large $K$, the collection of representative detectors can cover all discriminative trajectorylets. We call this collection the template trajectorylet detector set.

\section{Global descriptor and classification}
\label{sec: GL}
\label{sec: GL}
For the detectors in the template detector set, we evaluate their detection scores on each trajectorylet and max-pool those detection scores to obtain the action representation. Formally, let $ {\bf x } _i^j \in \mathbb{R}^n$ be the $j$-th trajectorylet of the $i$-th action, and $( {\bf w } _k, b_k)$ be the $k$-th detector in the template detector set.
We define the action representation for the $i$-th action ${\bf \Phi}( {\bf x } _i) = [\Phi_k( {\bf x } _i)]_{k=1,\ldots,K} $ as:

\begin{equation}
	\label{proj_encoding}
	{ \Phi}_k( {\bf x } _i) = \max_j {( {\bf w } _k^{\!\top}  {\bf x } _i^j + b_k)}, \; k=1, \cdots, K.
\end{equation}

We use a one-versus-all SVM to classify actions among the $C$ action classes $y_i \in \{1, \cdots, C \}$.

The learned feature mapping ${\bf \Phi}(\cdot)$ governed by the template detector set serves as a global descriptor of the action instance. It maps temporally continuous trajectorylets into a higher-level representation. Also, ${\bf \Phi}(\cdot)$ can not only map a complete sequence of action, but also works for a temporal sub-sequence. This allows us to build a temporal pyramid representation of the action instance. For a 3-level temporal pyramid, the sub-sequences are $F^{(p)}, p=1, \cdots, 7$, and the $k$-th dimension of subfeature ${\bf \Phi}^{(p)}( {\bf x } _i)$ for sub-sequence $p$ is

\begin{equation}
	\label{proj_encoding_TP}
	{ \Phi}^{(p)}_k( {\bf x } _i) = \max_{j \in F^{(p)}} {( {\bf w } _k^{\!\top}  {\bf x } _i^j + b_k)}.
\end{equation}

The concatenated ${\bf \Psi}(\cdot) = [{\bf \Phi}^{(1)}(\cdot)^{\!\top}, \cdots, {\bf \Phi}^{(7)}(\cdot)^{\!\top}]^{\!\top}$ incorporates the temporal information of the skeleton sequence. Therefore we are able to train a one-versus-all SVM with this feature that takes into account the global temporal information of the whole action sequence.

\section{Experiments}
\label{sec: EX}
We organize the experimental evaluation in four parts. We first compare our proposed method against other state-of-the-art methods on two standard datasets obtained from the Kinect sensor. Then we analyze the performance of our method under different parameter settings. Since our method consists of two modules, the trajectorylet descriptor and the template detector learning based middle-level feature representation, we conduct two experiments to separately evaluate their impact on the classification performance. To examine the first module, we compare our descriptor against the descriptor of \cite{MovingPose}, which is most related to our trajectorylet descriptor, by keeping the other settings of the recognition system the same. We also compare our descriptor with its several alternative variants.
To examine the second module, we compare our method with alternative way to obtain constructed from three state-of-the-art middle-level feature representation methods: VLAD \cite{VLAD10}, LSC \cite{LSC11}, and LLC \cite{LLC10}.

{\bf Implementation details:} The ESVMs are implemented by liblinear \cite{liblinear08}, which produces about 5 candidate detectors per second on an Intel Core i7 CPU at 3.40GHz. We set the regularization parameters as ${\lambda_1}=10$ and ${\lambda_2}=0.01$ for all ESVMs. There are on average $9,000$ and $30,000$ local descriptors in the negative sets for MSR Action3D and MSR DailyActivity3D respectively. The dimensionality of trajectorylets is reduced to 50\% percent of it by PCA. As the testing data will not be known in advance, the PCA coefficients $\mu$ and covariance matrix are learned from the training data only. Unless indicated otherwise, the length of trajectory descriptor is set to $L=5$. The regularization parameter for the final one-versus-all SVM is determined by a five-fold cross-validation. We apply a 3-level temporal pyramid on MSR DailyActivity3D only, because it contains complex actions which involves several sub-actions and the long-range temporal information can be useful in such a case.

\subsection{MSR Action3D}

\begin{table}
\begin{center}
\begin{tabular}{|l|l|l|}
\hline
AS1  & AS2 & AS3 \\
\hline\hline
 Horizontal arm wave & High arm wave & High throw  \\
Hammer & Hand catch & Forward kick  \\
Forward punch & Draw x & Side kick  \\
High throw & Draw tick & Jogging \\
Hand clap & Draw circle & Tennis swing \\
Bend & Two hand wave & Tennis serve \\
Tennis serve & Forward kick & Golf swing \\
Pickup \& throw & Side boxing & Pickup \& throw \\

\hline
\end{tabular}
\end{center}
\caption{The classes in the three action subsets of the MSR Action3D dataset.}
\label{tab: action_sets}
\end{table}

\begin{table}

\begin{center}
\begin{tabular}{|l|c|c|c|c|}

\hline
Protocol of \cite{3Dbag} & AS1  & AS2 & AS3 & Average\\
\hline\hline
3DBag \cite{3Dbag} & 72.9 & 71.9 & 79.2 & 74.7 \\
HO3DJ \cite{HOJ3D13} & 88.0 & 85.5 & 63.5 & 79.0 \\
EigenJoints \cite{eigenjoints12} & 74.5 & 76.1 & 96.4 & 82.3 \\
HOD \cite{HOD13} & 92.4  & 90.1 & 91.4 & 91.2 \\
Lie Group \cite{LieGroup14} & 95.3 & 83.9 & 98.2 & 92.5  \\
EJS \cite{Evo14} & 91.6 & 90.8 & 97.3 & 93.2  \\
Moving Pose \cite{MovingPose} \footnotemark[2]   & $\mathbf{96.4}$ &91.6 &99.1 &95.7 \\

Ours & $\mathbf{96.4}$ & $\mathbf{97.5}$ & $\mathbf{100.0}$ & $\mathbf{97.9}$ \\
\hline
\end{tabular}
\end{center}
\caption{Results on 3 subsets of the MSR Action3D dataset.}
\label{tab: action_all}
\end{table}

 \footnotetext[2]{We use the code of \cite{MovingPose} to obtain this result, as the original work did not report the results according to the protocol of \cite{3Dbag}.}

The MSR Action3D dataset consists of human actions expressed with skeletons composed of 20 3D body joint positions in each frame. The 20 joints are connected by 19 limbs. There are 20 action classes performed by 10 subjects for 2 or 3 times each, making up 567 action instances. Each action instance contains a temporal sequence of a moving skeleton, usually in 30-50 frames. As in \cite{Actionlet12} and \cite{MovingPose}, we drop 10 instances because they contain erroneous data. The experiment setup is that of a cross-subject test \cite{3Dbag}, {\rm i.e. }  instances of half of the subjects are used for training and instances of the other half subjects are used for testing. We construct $H_t$ with top responding $N_A=50$ trajectorylets, and select $M_A=10$ best detectors for each training instance. We use the clustering method of section~\ref{subsec:Cluster} to obtain the template trajectorylet detector set. The final number of template detectors is set to $K=500$.

In Table~\ref{tab: action_all}, we compare our approach with other state-of-the-art methods using the protocol of \cite{3Dbag}, by which the 20 action classes are grouped into 3 action subsets AS1, AS2, and AS3. The training and testing is performed on each action set separately. AS1 and AS2 group actions with similar movements while AS3 group complex actions. The action classes of each action subset are listed in Table~\ref{tab: action_sets}. On average, our proposed method is more accurate than all other methods. On AS2, all other methods get moderate accuracy and in contrast our method outperforms the second best by 5.9\%. Note that on AS3, our method achieves perfect recognition.

\begin{table}
\begin{center}
\begin{tabular}{|l|c|}
\hline
Protocol of \cite{Actionlet12} & Accuracy \\
\hline\hline
Recurrent Neural Network \cite{RNN11} & 42.5 \\
Dynamic Temporal Warping \cite{DTW06} & 54.0 \\
Canonical Poses \cite{Canonical13} & 65.7 \\
DBM+HMM  \cite{DBM_HMM14} & 82.0 \\
JAS (skeleton data only) \cite{jointangles13} & 83.5 \\
Actionlet Ensemble \cite{Actionlet12} & 88.2 \\
HON4D \cite{HON4D} & 88.9 \\
Lie Group \cite{LieGroup14} & 89.5  \\
LDS   \cite{bio13} \footnotemark[3]      & 90.0     \\
Pose based \cite{Pose_based13} & 90.2 \\
Moving Pose \cite{MovingPose}  & 91.7 \\
Ours & $\mathbf{95.9}$\\
\hline
\end{tabular}
\end{center}
\caption{Results on the entire MSR Action3D dataset.}
\label{tab: action}
\end{table}

 \footnotetext[3]{ It should be noted that the result of \cite{bio13} here is not obtained under the same setting as ours. This approach selected a subset of 17 actions
performed by 8 subjects, 5 for training and 3 for testing, consisting of 379 action instances in total.}

In Table~\ref{tab: action}, a more challenging protocol of \cite{Actionlet12} is used. Here the model is trained and tested over all 20 action classes. The results show that our method still obtains a highly accurate recognition rate, outperforming the current best state-of-the-art by a margin of 4.2\%. The confusion matrix of our method on this dataset under the second protocol is displayed in Figure~\ref{fig: cm_action}, where 16 of 20 action classes are perfectly classified. The only highly misclassified class is \emph{hammer}, because its distinctive pattern involves human-object interaction, which is not captured by the skeleton data.

\begin{figure*}[h]
\begin{center}
  \includegraphics[width=0.8\linewidth]{./fig/cm1.png}
\end{center}
   \caption{Confusion matrix of our approach on the MSR Action3D dataset: except for the \emph{hammer} class, all other action classes are classified with more than 80\% accuracy. 16 out 20 action classes are perfectly classified.}
	\label{fig: cm_action}
\end{figure*}

\subsection{MSR DailyActivity3D}
In MSR DailyActivity3D, there are 16 action classes performed by 20 subjects twice, making up 320 action instances. Each subject performs an action class in two variants ({\rm e.g. } sitting versus standing, or in front of versus behind an object). This dataset has longer sequences, usually in 100-300 frames. We still follow the cross-subject test in protocol of \cite{Actionlet12} and \cite{MovingPose}, where training and testing are conducted over all action classes. Because this dataset contains more local information than MSR Action3D, we construct $H_t$ with top responding $N_A=50$ trajectorylets, select $M_A=15$ best detectors for each training instance, and reduce the final number of clustered detectors to $K=500$.

\begin{table}[h]

\begin{center}
\begin{tabular}{|l|c|}
\hline
Methods & Accuracy  \\
\hline\hline
Dynamic Temporal Warping \cite{DTW06} & $54.0$ \\
Actionlet Ensemble (skeleton data only) \cite{Actionlet12} & $68.0$ \\
Moving Pose \cite{MovingPose} \footnotemark[4] & $70.6$  \\
Ours & $\mathbf{75.0}$\\
\hline
\end{tabular}
\end{center}
\caption{Results on the MSR DailyActivity3D dataset.}
\label{tab: activity}
\end{table}
 \footnotetext[4]{Although the reported result in \cite{MovingPose} is 73.8\%,  we never achieved this accuracy with their code due to environmental factors. For a fair comparison, we used the result 70.6\%, which is the best performance under the same environment and setting with our approach.}

We compare our approach with other state-of-the-art methods in Table~\ref{tab: activity}. As the purpose of this experiment is to address skeleton-based action recognition, some best reported results \cite{HON4D, Actionlet12} on this dataset using additional RGB-D data are not comparable to our method, and therefore we cite the result of \cite{Actionlet12} using only skeleton data.  Although MSR DailyActivity3D share the same data structure as the MSR Action3D, it is much more challenging because: 1) the activities are complex combinations of multiple sub-actions, 2) human-object interaction information is not available in skeleton data, 3) partial occlusion by interacting objects causes the skeleton data to be highly noisy. However, the results show that our approach still outperforms all other state-of-the-art methods.  As shown in Figure~\ref{fig: cm_activity}, most of the poorly classified actions involves interaction with objects, such as \emph{read book}, \emph{call cellphone}, and \emph{use laptop}. On the other hand, non-interactive action classes like \emph{cheer up}, \emph{walk}, and \emph{sit down}, are recognized with high accuracy. This demonstrates that our method is able to capture distinctive patterns of actions in terms of ``movement'', but may be confused if some actions share similar ``movement'' patterns despite the presences of different interacting objects, because they are not described in the skeleton data.

\begin{figure}[t]

\begin{center}
   \includegraphics[width=1.05\linewidth]{./fig/cm2}
\end{center}
   \caption{Confusion matrix of our approach on the MSR DailyActivity3D dataset: although this is a challenging dataset for skeleton-based action recognition, 11 out of 16 classes are classified with more 70\% accuracy.}
\label{fig: cm_activity}
\end{figure}

\subsection{Parameter analysis}
In this section we analyse how the parameter settings affect the performance. Using the same protocol of \cite{Actionlet12}, we provide results of MSR Action3D dataset from other parameter settings. Figure \ref{fig: K_action} illustrates the performances of our method as $K$ ranges from \{25, 50, 100, 200, 300, ... , 1000\}, while keeping  $N_A=50$ and $M_A=10$. When we set the size of detector set more than 500, the results tend to converge to a value above 94.5\%. Table \ref{tab: compare_N_M_action} presents results of choosing different pairs of $M_A$ and $N_A$ while keeping $K=500$.

\begin{figure}[h]
\begin{center}
   \includegraphics[width=1.05\linewidth]{./fig/K_action.eps}
\end{center}
   \caption{Recognition accuracies obtained from varying $K$ on the MSR Action 3D dataset: when $K \geq 500$ the results become  stable.}
	\label{fig: K_action}
\end{figure}

\begin{table}
\begin{center}
\begin{tabular}{|l|c|c|c|c|c|c|}
\hline
\theadfont\diagbox[width=6em]{$M_A$}{$N_A$} & \thead{5}  & \thead{10} & \thead{15} & \thead{20} & \thead{30} & \thead{50} \\
\hline\hline
5 & 91.7  &  & & & & \\
10 & 92.7 & 93.4 & & & & \\
20 & 93.1 & 93.1 &94.1 &94.8 & & \\
30  & 94.8  & 95.2 & 94.1 & 93.8 & 94.8 & \\
50  & 95.5  & 95.9  & 95.9  &94.8  & 94.8 & 94.2\\

\hline
\end{tabular}
\end{center}
\caption{Results from different pairs of the $M_A$ and $N_A$ on MSR Action3D: we can obtain the best performance from multiple choices.}
\label{tab: compare_N_M_action}
\end{table}

For the MSR DailyActivity3D dataset, Figure \ref{fig: K_activity} illustrates the performances of our method as K ranges from \{25, 50, 100, 200, 300, ... , 1000\}, while keeping  $N_A=50$ and $M_A=15$. When $K$ is set to more than 500, the results become stable.  The effect of choosing different pairs of $M_A$ and $N_A$ is listed in in Table \ref{tab: compare_N_M_activity}. When $M_A$ is large enough, the results variation becomes small. It can be observed that, on both datasets, there are multiple choices of parameters that are able to produce the optimal result and this verifies the robustness of our approach.

Table \ref{tab: TP_levels} shows the results under different temporal pyramid settings for the two datasets. A typical 3-level pyramid is the best choice for MSR DailyActivity3D as low level pyramids fail to grasp the temporal information while higher level ones brings too much noise. On the other hand, when temporal pyramid is applied to MSR Action3D, the performance is worsened.
\begin{figure}[h]
\begin{center}
   \includegraphics[width=1.05\linewidth]{./fig/K_activity.eps}
\end{center}
   \caption{Recognition accuracy obtained from varying $K$ on the MSR Daily Activity 3D dataset: when $K>400$ the results become stable.}
	\label{fig: K_activity}
\end{figure}

\begin{table}
\begin{center}
\begin{tabular}{|l|c|c|c|c|c|c|}
\hline
\theadfont\diagbox[width=6em]{$M_A$}{$N_A$} & \thead{5}  & \thead{10} & \thead{15} & \thead{20} & \thead{30} & \thead{50} \\
\hline\hline
5 & 68.7  &  & & & & \\
10 & 68.1 & 69.4 & & & & \\
20 & 68.7 & 71.2 & 70.0 & 69.4 & & \\
30  & 70.0 & 73.1 & 73.8 &71.2 & 71.2 & \\
50  & 73.1 & 74.3 & 75.0 &75.0 & 74.3 & 71.9\\

\hline
\end{tabular}
\end{center}
\caption{Results from different pairs of the $M_A$ and $N_A$ on MSR DailyActivity3D.}
\label{tab: compare_N_M_activity}
\end{table}

\begin{table}
\begin{center}
\begin{tabular}{|l|c|c|c|c|}
\hline
TP level & 1  & 2 & 3 & 4\\
\hline\hline
Action & 95.9  & 92.4 & 89.7  & N/A\\
DailyActivity & 66.3  & 70.6 & 75.0  & 68.8\\

\hline
\end{tabular}
\end{center}
\caption{Results obtained from different temporal pyramid levels on MSR Action3D and MSR DailyActivity3D datasets.}
\label{tab: TP_levels}
\end{table}

\subsection{Power of local trajectorylet descriptor}
The moving pose descriptor proposed in \cite{MovingPose} captures local information at frame-level of human skeleton actions. Our trajectorylet can be seen as a natural extension of it in the sense that we extend the dynamic information from frame-level range to a longer temporal range. In order to demonstrate the power of our descriptor we now apply our template detector learning framework to moving pose descriptor and compare its performance with that of trajectorylet.

In order to evaluate the effect of varying $L$ on performance we have varied the length our trajectorylet from (3, 5, 7). Table \ref{tab: compare_feature} shows that using the same detector learning and classification approach, trajectorylets achieve better results on both datasets for all tested values of $L$. As seen, this extension of moving pose descriptor is superior over the original design. It is worth noting that performance does not necessarily improve as the length of trajectorylets increases. A moderate length of trajectorylet ($L=5$) leads to the best performance.

We also test the effect of using different components of the trajectorylet descriptor. In our experiment, we examine the performance of single dynamic components, including static pose $ {\bf x } _0$, relative joint displacement $ {\bf x } _1$, velocity $ {\bf x } _2$, and their combinations. We also further define an acceleration component analogous to \eqref{EQ:adjacent_frame1} and \eqref{EQ:adjacent_frame2}:
\begin{align}
\label{EQ:Com_acc}
 &  {\bf x } _{3}^{t_0} = [ {\Delta^3{\mathbf j}^{t_0+2}}^{\!\top}, \cdots, {\Delta^3{\mathbf j}^{t_0+L-1}}^{\!\top} ]^{\!\top} \in  \mathbb{R}^{((L-3) \times 3J)}, \\
 &
\Delta^3{\mathbf j}^{t_0+i}=\Delta^2{\mathbf j}^{t_0+i} - \Delta^2{\mathbf j}^{t_0+i-1}, \, i=3, \cdots, L-1. \notag
\end{align}

 The results of varying settings of a trajectorylet with $L=5$ are listed in Table \ref{tab: compare_level}.  We find that the dynamic components of $ {\bf x } _1$ and $ {\bf x } _2$ alone do not show promising results. However, when combined with static $ {\bf x } _0$, the performance is significantly improved. Table \ref{tab: compare_level} also shows that the additional acceleration component in \eqref{EQ:Com_acc} does not improve the performance.
 %

\begin{figure*}[h]
\vspace{-0.0cm}
\begin{center}
\scalebox{0.6}{
\begin{tabular}{@{}c@{}c@{}c@{}c@{}c}
\includegraphics[width=0.18\linewidth]{./fig/representative_trajectorylets/traja1s3e1f19.eps} \ &
\includegraphics[width=0.18\linewidth]{./fig/representative_trajectorylets/traja2s3e1f13.eps} \ &
\includegraphics[width=0.15\linewidth]{./fig/representative_trajectorylets/traja3s3e2f21.eps} \ &
\includegraphics[width=0.16\linewidth]{./fig/representative_trajectorylets/traja4s5e2f12.eps} \ &
\includegraphics[width=0.18\linewidth]{./fig/representative_trajectorylets/traja5s1e1f24.eps} \\
high wave\ &
hori. wave \ &
hammer \ &
hand catch \ &
forward punch\\
\includegraphics[width=0.13\linewidth]{./fig/representative_trajectorylets/traja6s1e2f18.eps} \ &
\includegraphics[width=0.18\linewidth]{./fig/representative_trajectorylets/traja7s3e3f13.eps} \ &
\includegraphics[width=0.16\linewidth]{./fig/representative_trajectorylets/traja8s1e3f17.eps} \ &
\includegraphics[width=0.16\linewidth]{./fig/representative_trajectorylets/traja9s1e1f18.eps} \ &
\includegraphics[width=0.21\linewidth]{./fig/representative_trajectorylets/traja10s4e3f7.eps} \\
high throw\ &
draw x \ &
draw tick \ &
draw circle \ &
hand clap\\
\includegraphics[width=0.21\linewidth]{./fig/representative_trajectorylets/traja11s1e3f8.eps} \ &
\includegraphics[width=0.18\linewidth]{./fig/representative_trajectorylets/traja12s5e3f7.eps} \ &
\includegraphics[width=0.11\linewidth]{./fig/representative_trajectorylets/traja13s2e2f4.eps} \ &
\includegraphics[width=0.15\linewidth]{./fig/representative_trajectorylets/traja14s1e1f18.eps} \ &
\includegraphics[width=0.20\linewidth]{./fig/representative_trajectorylets/traja15s2e2f15.eps} \\
2-hand wave\ &
boxing \ &
bend \ &
forward kick \ &
side kick\\
\includegraphics[width=0.12\linewidth]{./fig/representative_trajectorylets/traja16s3e2f16.eps} \ &
\includegraphics[width=0.19\linewidth]{./fig/representative_trajectorylets/traja17s4e2f24.eps} \ &
\includegraphics[width=0.13\linewidth]{./fig/representative_trajectorylets/traja18s5e3f8.eps} \ &
\includegraphics[width=0.15\linewidth]{./fig/representative_trajectorylets/traja19s5e2f10.eps} \ &
\includegraphics[width=0.17\linewidth]{./fig/representative_trajectorylets/traja20s5e3f12.eps} \\

jog\ &
t. swing \ &
t. serve \ &
g. swing \ &
pick up \& throw\\

\end{tabular}
}
\end{center}
\caption{Some examples responding on the template detector set of MSR Action3D. The black curves represent current trajectorylets of the red skeleton. The fact that the our approach identifies discriminative patterns of movement seems clear.}
\label{fig:rep_trajectorylets}
\end{figure*}

\begin{table}[ht]
\begin{center}
\begin{tabular}{|l|c|c|}
\hline
Descriptors & MSR Action   & MSR DailyActivity\\
\hline\hline
Moving Pose \cite{MovingPose} & 91.7  & 71.3\\
Ours($L=3$) & 93.1 & 72.5\\
Ours($L=5$) & $\mathbf{95.9}$ & $\mathbf{75.0}$\\
Ours($L=7$) & $\mathbf{95.9}$ & 73.1\\

\hline
\end{tabular}
\end{center}
\caption{Comparison of using different descriptors.}
\label{tab: compare_feature}
\end{table}

\begin{table}[ht]
\begin{center}
\begin{tabular}{|l|c|c|}
\hline
Component & MSR Action   & MSR DailyActivity\\
\hline\hline
$ {\bf x } _0$ & 92.4 & 72.5\\
$ {\bf x } _1$ & 91.7 & 50.3\\
$ {\bf x } _2$ & 90.3 & 42.5\\
$( {\bf x } _0,  {\bf x } _1)$ & 93.8 & 73.1\\
$( {\bf x } _0,  {\bf x } _1,  {\bf x } _2)$ & $\mathbf{95.9}$ & $\mathbf{75.0}$ \\
$( {\bf x } _0,  {\bf x } _1,  {\bf x } _2,  {\bf x } _3)$ & $\mathbf{95.9}$  & 74.3 \\

\hline
\end{tabular}
\end{center}
\caption{Comparison of different using different components of trajectorylet ($L=5$).}
\label{tab: compare_level}
\end{table}

\begin{table}[ht]
\begin{center}
\begin{tabular}{|l|c|c|}
\hline
Method & MSR Action   & MSR DailyActivity\\
\hline\hline
VLAD \cite{VLAD10} & $83.1 $  & $51.9$\\
LLC \cite{LLC10} & $90.7 $ & $65.6 $\\
LSC \cite{LSC11} & $92.1 $ & $66.9 $\\
Ours & $\mathbf{95.9}$ & $\mathbf{75.0 }$\\
\hline
\end{tabular}
\end{center}
\caption{Comparison of feature learning methods.}
\label{tab: learning_compare}
\end{table}

\subsection{Power of template detector learning}

Our method generates action representation from learned detector set of discriminative trajectorylets. In this section, we compare this method with three state-of-the-art bag-of-feature techniques that learn middle-level feature from the same local trajectorylet feature: VLAD (vector of locally aggregated descriptors)\cite{VLAD10}, LLC (locality-constrained linear coding)\cite{LLC10}, and LSC (localized soft-assignment coding) \cite{LSC11}.

We train codebook of the same size $K=128$ with k-means for all three methods, and set the neighbourhood size of codewords as $\kappa=10$ for LSC and LLC. The results listed in Table~\ref{tab: learning_compare} show, for the task of action recognition, our proposed feature learning framework produces the most discriminative action representation, compared with the state-of-the-art methods. Figure~\ref{fig:rep_trajectorylets} illustrates some  trajectorylets fired on the template detector set of MSR Action3D. It is clear that they show representative patterns for the corresponding action classes.

\section{Conclusion}
\label{sec: CONC}
This work describes an effective skeleton-based action approach that achieves high accuracy on the relevant benchmark datasets. The keys to this performance are two factors. We propose trajectorylet, a novel local descriptor that captures static and dynamic information in a short interval of joint trajectories. We also devise a novel framework to generate robust and discriminative representation for action instances by learning a set of distinctive trajectorylet detectors. On two benchmark datasets acquired from the Kinect sensor, our method outperforms, to our knowledge, all existing approaches by a significant margin. We also separately demonstrate the validity of our local descriptors and template detector learning method. To further expand our framework, we plan to incorporate local temporal information to enable real-time detection, as well as investigate the RGB data to study the involvement of human-object interactions.

\ifCLASSOPTIONcaptionsoff
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{\small
\bibliographystyle{ieee}

redpajama_set_name: RedPajamaArXiv


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text:
Ridley Scott's "Raised by Wolves" Coming to HBO Max
The series centers on two androids tasked with raising human children on a mysterious virgin planet.
[via press release from WarnerMedia]
Sci-fi Project Marks Scott's Series Directorial Debut
Los Angeles, October 29, 2019- Announced today at the HBO Max WarnerMedia Day, Raised by Wolves, a serialized sci-fi series from master storyteller and filmmaker Ridley Scott (The Martian, Blade Runner, Alien: Covenant) will premiere on the direct-to-consumer offering.
The epic serialized sci-fi series is executive produced and directed by Ridley Scott (The Martian, Blade Runner, Alien: Covenant). Acclaimed writer Aaron Guzikowski, the visionary behind the highly lauded film Prisoners, created the 10-episode series and serves as showrunner and writer for the project, which recently wrapped production in Cape Town, South Africa. The series centers on two androids tasked with raising human children on a mysterious virgin planet. As the burgeoning colony of humans threatens to be torn apart by religious differences, the androids learn that controlling the beliefs of humans is a treacherous and difficult task.
"As we strive to set a high creative bar for HBO Max, there's no better person to set that bar than Ridley Scott as he delivers his first season of yet another incredible vision realized," said Kevin Reilly, chief content officer, HBO Max and president TBS, TNT, and truTV.
"The breathtaking worlds, compelling characters and intricate storylines that Ridley, Aaron and team have created for this project are mesmerizing," added Sarah Aubrey, head of original content, HBO Max.
The cast includes Travis Fimmel (Vikings), Amanda Collin (A Conspiracy of Faith, A Horrible Woman), Abubakar Salim (Fortitude, Jamestown), Winta McGrath (Aloft, Red Dog: True Blue), Niamh Algar (The Virtues, Calm With Horses), Matias Varela (Narcos, Point Break), Felix Jamieson(Game of Thrones, The Ghost), Ethan Hazzard (The Long Song, Damned), Jordan Loughran (The Infiltrator, Emerald City), Aasiya Shah (Call the Midwife, Unforgotten) and Ivy Wong (Star Wars: Rogue One, Maleficent 2).
The series is produced by Scott's Scott Free Productions, with Scott, Guzikowski, David W. Zucker (The Man in the High Castle), Jordan Sheehan (The Terror), Adam Kolbrenner (Prisoners) and Mark Huffam (The Martian) serving as executive producers.
About HBO Max
HBO Max is WarnerMedia's direct-to-consumer offering debuting in spring 2020. With 10,000 hours of curated premium content anticipated at launch, HBO Max will offer powerhouse programming for everyone in the home, bringing together HBO, a robust slate of new original series, key third-party licensed programs and movies, and fan favorites from WarnerMedia's rich library including Warner Bros., New Line, DC, CNN, TNT, TBS, truTV, Turner Classic Movies, Cartoon Network, Adult Swim, Crunchyroll, Rooster Teeth, Looney Tunes and more. Sign up for updates at HBOMax.com.
About WarnerMedia
WarnerMedia is a leading media and entertainment company that creates and distributes premium and popular content from a diverse array of talented storytellers and journalists to global audiences through its consumer brands including: HBO, HBO Now, HBO Max, Warner Bros., TNT, TBS, truTV, CNN, DC, New Line, Cartoon Network, Adult Swim, Turner Classic Movies and others. WarnerMedia is part of AT&T Inc. (NYSE:T).
About Scott Free TV
Scott Free Television is the television production company of acclaimed filmmaker Ridley Scott. In the 20 years since Scott Free was formed, the company has earned over 100 Emmy nominations with 22 wins, and 28 Golden Globe nominations for its television projects. Current productions include the forthcoming flagship HBO Max sci-fi series, RAISED BY WOLVES, the first-ever to be helmed by Ridley Scott, a drama about androids raising human children in an outer space colony; Amazon's epic drama, THE MAN IN THE HIGH CASTLE, concluding its climactic fourth and final season; recently ordered season four of THE GOOD FIGHT, the critically-acclaimed CBS All Access spin-off of THE GOOD WIFE; a second season of the AMC historical anthology series THE TERROR: INFAMY; the second season of the eponymous series adaptation of George Pendle's book STRANGE ANGEL on CBS All Access; the Fox drama THE PASSAGE, based on Justin Cronin's post-apocalyptic trilogy; and the National Geographic limited series THE HOT ZONE, based on the Richard Preston book and starring Julianna Margulies,and a new adaption of Charles Dickens' classic A Christmas Carol set to premiere in December, starring Guy Pearce and Andy Serkis. Other hailed Scott Free productions include the BAFTA-winning TABOO for FX/BBC1, Emmy and Golden Globe-nominated, Peabody-winning drama, "The Good Wife," which ran on CBS for seven seasons; two seasons of the esteemed PBS Civil War medical drama, "Mercy Street;" "Numb3rs," which ran for six successful seasons on CBS; and the acclaimed Emmy-nominated National Geographic channel series of "Killing" telefilms: "Killing Jesus" "Killing Kennedy" "Killing Reagan" and the docudrama "Killing Lincoln." Notable long form programs include "Klondike" for Discovery; the Emmy and Golden Globe-nominated "The Pillars of the Earth" for Starz; "World Without End" for Reelz; Emmy-nominated "Into the Storm" for HBO; Emmy-nominated "The Andromeda Strain" and "Coma" for A&E; the Golden Globe-nominated "The Company" at TNT; and a feature-length digital series for X-Box/Microsoft, "Halo: Nightfall." Acclaimed non-fiction programs include CNN's "Crimes of the Century;" and longform documentaries such as the Emmy-winning special "Gettysburg" for History Channel; Hulu's "Re: Generation Music Project," directed by Amir Bar-Lev; "I Am Dying," directed by Dan Lindsay and TJ Martin for National Geographic; and Apple's "Clive Davis: The Soundtrack of Our Lives," directed by Chris Perkel, which opened the 2017 Tribeca Film Festival. With offices in Los Angeles and London, Scott Free works closely with RSA Films, one of the world's largest and most successful commercial production houses.
· RAISED BY WOLVES (HBO MAX)


text:
Кислово́дськ () — тупикова залізнична станція Північно-Кавказької залізниці на двоколійній електрифікованій лінії  Мінеральні Води — Кисловодськ (завдожки 64 км). Розташована у місті Кисловодськ Ставропольського краю.

Історія
17 травня 1894 року відкритий регулярний рух поїздів на дільниці Мінеральні Води — Кисловодськ.

1936 року станція електрифікована постійним струмом (=1,5 кВ). У листопаді 1936 року від станції Мінеральні Води здійснив тестовий рейс перший електропоїзд. Живлення енергією відбувалось від Баксанської ГЕС — першої найбільшої гідроелкетростанції Північного Кавказу.

1964 року станція  переведена на змінний струм (=3 кВ), що сприяло курсування поїздів під тягою двосистемних електровозів типу ВЛ61Д та ВЛ82, до Мінеральних Вод курсували електропоїзди постійного струму. 2006 року ця дільниця переведена на змінний струм (~25 кВ) і понині використовуються  електропоїзди змінного струму.

Інфраструктура
Станція тупикового типу з 4 високими й 1 низькою платформами, між якими прокладено по одній колії. Прохід на платформу приміських електропоїздів здійснюєтся через турнікети. Будівля вокзалу розташована ліворуч від колії. За 1,5 км на північ від платформ, поруч із зупинним пунктом Минутка, розташований відстійний парк для локомотивів та составів поїздів.

Пасажирське сполучення
Приміські електроїзди:

Поїзди далекого сполучення:

Сезонний рух поїздів:

Зміни руху у пасажирському сполученні:
 1 листопада 2011 року призначений новий пасажирський поїзд № 367/368 до станції Кіров.
 21 січня 2012 року скасований пасажирський поїзд № 489/490 до станції Сімферополь.
 27 травня 2012 року скасований пасажирський поїзд № 361/362 до станції Самара.
 26 травня 2013 року змінена нумерація та категорія пасажирського поїзда з № 629/630 на № 69/70 до Ростова.
 26 травня 2013 року скасований пасажирський поїзд № 419/420 до станції Орськ.
 26 травня 2013 року скорочений маршрут руху пасажирського поїзда № 355/356 Мінськ — Кисловодськ до станції Мінеральні Води та змінена нумерація на № 445/446.
 1 червня 2014 року скасований пасажирський поїзд № 269/270) до станції Тамбов.
 1 червня 2014 року змінена категорія пасажирського поїзда № 197/198 на № 57/58 до станції Іркутськ-Пасажирський.
 1 червня 2014 року змінена категорія пасажирського поїзда № 193/194 на № 113/114 до станції Челябінськ.
 1 червня 2014 року скасований швидкий поїзд № 27/28 до Москви, що прямував територією України.
 26 червня 2014 року призначений новий швидкий поїзд сезонного курсування № 117/118 до станції Челябінськ.
 18 серпня 2014 року скасований швидкий поїзд «Каштан» № 25/26 до станції Київ-Пасажирський.
 1 вересня 2014 року скасований сезонний швидкий поїзд № 117/118 до станції Челябінськ.
 1 червня 2015 року пасажирський поїзд № 643/644 Кисловодськ — Адлер переведений до категорії «фірмовий поїзд».
 1 травня 2017 року змінена нумерація та категорія пасажирського поїзда з № 69/70 на № 809/810, що прямує до станції Ростов-Головний, також змінений рухомий склад на електропоїзди «Ластівка».
 8 грудня 2018 року призначений швидкий поїзд № 367/368 до станції Орськ.
 9 грудня 2018 року змінений кінцевий пункт прибуття поїзда № 144/143 Кисловодськ — Москва, який прибуває на Курський вокзал замість Казанського вокзалу.
 29 червня 2020 року призначений пасажирський поїзд № 525/526 до станції Сімферополь.
 16 січня 2021 року призначений новий швидкий двоповерховий поїзд № 175/176) до станції Воронеж I.
 3 та 10 травня 2021 року на станцію прибував туристичний поїзд № 929/930 Москва-Павелецька — Кисловодськ — Москва-Київська.

Галерея

Див. також
 Кисловодськ
 Північно-Кавказька залізниця
 Російські залізниці

Примітки

Панорама

Засновані в Росії 1894
Кисловодськ
Північно-Кавказька залізниця
Залізничні станції Росії
Станції Північно-Кавказької залізниці
Об'єкти культурної спадщини Росії регіонального значення

redpajama_set_name: RedPajamaWikipedia


text:
Facts about Will Smith slapping Chris Rock at the 2022 Oscars Part 2
March 29, 2022 Robith Entertaiment
Will Smith is in danger of losing his first Oscar
Even though he won an Oscar for the first time, Will Smith was in danger of losing his trophy at the event. This is of course the aftermath of Smith's actions that slapped Chris Rock. Moreover, after the event, the Academy Awards made a statement that they condemned all forms of violence.
In addition, in 2017 the Academy Awards issued a new code of ethics as a result of the increasing number of sexual and violence cases in the Hollywood industry. In this code, Academy Awards members must have good ethics, uphold humanity, and create a mutually supportive environment.
The Academy Awards also criticizes individuals who use their power for certain purposes. In addition, they also condemn various acts of violence or discrimination related to gender, sexual orientation, race, ethnicity, disability, religion, and nationality. So, it is very likely that the Academy Awards will revoke Will Smith's status as "Best Actor" because of the incident.
Even though it was done in defense of his wife, Will Smith's act of slapping Chris Rock was clearly violence. Moreover, his actions were witnessed by millions of people, both those who were present in person at the location or who watched on the screen.
By law, Chris Rock should be able to sue Smith for his actions and seek damages or even put him in jail. However, according to Variety, Chris Rock is reportedly not going to sue Will Smith at all for the incident.
This was even revealed directly by the Los Angeles police who were aware of the incident of Chris Rock's slapping by Will Smith at the 2022 Oscars. Even so, the police claimed to be ready to help if Rock changed his mind and wanted to report Smith.
The number of viewers for the 2022 Oscars skyrocketed after the Will Smith-Chris Rock incident
Despite the various controversies, there is one "positive" impact for the hosting of the 2022 Oscars due to the Will Smith-Chris Rock incident. The reason is, based on a report by Jacob King, who is an analyst, the number of spectators at this year's Oscars rose sharply after the beating incident.
Based on data that Jacob King shared via his personal Twitter, the number of Oscar 2022 viewers had experienced ups and downs and only reached 9.6 million people before the incident occurred. However, after Smith slapped Rock, the number of viewers jumped sharply to 17.4 million. In fact, this number is much higher than the 2021 Oscars which only had 9.85 million viewers.
Facts about Will Smith slapping Chris Rock at the 2022 Oscars
Will Smith Almost Got Expelled from the 2022 Oscars After Slapping Chris Rock