Answers.Body: <p>In response to your question, I recommend using YOLOv8 for pose detection. It's a highly efficient and low-cost model for pose estimation, and it performs well in real-time applications. You can use it to extract key points from the video frames. you can use any other pose detection model it is a suggestion for you. like yolov8 is easy to use and highly accurate also it gives around 17 points of human pose</p>
  <p>However, remember that many human actions depend on movements made in the past, so a simple classification of individual frames might not be enough for accurate action recognition. Instead, I suggest training a CNN-LSTM-Attention model.</p>
  <p>=&gt; CNN will help capture spatial features.</p>
  <p>=&gt; LSTM (Long Short-Term Memory) will account for temporal dependencies (previous movements).</p>
  <p>=&gt; The attention mechanism can further enhance the model’s ability to focus on the most important frames/key points for classification.</p>
  <p>This architecture would allow you to classify actions based on sequences of key points extracted from videos, giving you much better accuracy than classifying single frames or individual poses.</p>
  
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Answers.CreationDate: 2024-09-06T12:43:58.210Z
Answers.LastActivityDate: 2024-09-06T12:43:58.210Z
Answers.UserDisplayName: Keval Pandya
Answers.Body: <p>My suggestion is to go with 1st option. reason is you will get to know much about data and initial stage will find some challenges in developing the model, over a period of time you will get to better results after hypertunning. Please go through <a href="http:////https://machinelearningmastery.com/deep-learning-models-for-human-activity-recognition/" rel="nofollow noreferrer">article</a> , ignore you have already read this article</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-12-27T13:25:06.870Z
Answers.Text: Thanks for the article will read this. Any idea how I get a lot of images or how many I actually need?
Answers.UserDisplayName: user1007522
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-12-30T07:11:43.257Z
Answers.Text: Are you looking for label data with images or just images, have found one data set, [link](http://vision.stanford.edu/Datasets/40actions.html).
Answers.UserDisplayName: Muralidhar A
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-12-30T10:41:51.487Z
Answers.Text: Thank you. For me it doesn't matter I can label them myself. Like for example pictures of people bending their arms. When searching for this on google not a lot of pictures show up. In the database you shared per action 280 pictures exist.
Answers.UserDisplayName: user1007522
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Answers.UserDisplayName: Muralidhar A
Body: <p>I'm quite new to machine learning (I followed the Coursera course of Andrew Ng and now starting deeplearning.ai courses).</p>
  
  <p>I want to classify human actions real-time like:</p>
  
  <ul>
  <li>Left-arm bended</li>
  <li>Arm above shoulder</li>
  <li>...</li>
  </ul>
  
  <p>I first did some research for pre-trained models, but I didn't find any.
  Because I'm still quite new, I want to have advice about how I should solve this.</p>
  
  <ol>
  <li><p>I thought maybe I need to create for every action enough pictures and from there on I can do image classification. </p></li>
  <li><p>Or I use PoseNet from TensorFlow so that I have the pose estimation points. And from there on I create videos of a couple of seconds with every pose I want to track and I save the estimation points. From there on, I use a classification algorithm (neural network) to classify those points. </p></li>
  </ol>
  
  <p>What is the most efficient option or are they both bad and is there a better way to do this?</p>
  
Tags: <neural-networks><classification><image-recognition><video-classification>
Title: How to classify human actions?
UserDisplayName: user1007522
ThreadText: # How to classify human actions?
  
  (Asked by: username_0 on 2019-12-25)
  
  I'm quite new to machine learning (I followed the Coursera course of Andrew Ng and now starting deeplearning.ai courses).
  
  I want to classify human actions real-time like:
  
  * Left-arm bended
  * Arm above shoulder
  * ...
  
  I first did some research for pre-trained models, but I didn't find any.
  Because I'm still quite new, I want to have advice about how I should solve this.
  
  1. I thought maybe I need to create for every action enough pictures and from there on I can do image classification.
  2. Or I use PoseNet from TensorFlow so that I have the pose estimation points. And from there on I create videos of a couple of seconds with every pose I want to track and I save the estimation points. From there on, I use a classification algorithm (neural network) to classify those points.
  
  What is the most efficient option or are they both bad and is there a better way to do this?
  
  **Tags:** neural-networks, classification, image-recognition, video-classification
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2024-09-06. Score: 0
  
  In response to your question, I recommend using YOLOv8 for pose detection. It's a highly efficient and low-cost model for pose estimation, and it performs well in real-time applications. You can use it to extract key points from the video frames. you can use any other pose detection model it is a suggestion for you. like yolov8 is easy to use and highly accurate also it gives around 17 points of human pose
  
  However, remember that many human actions depend on movements made in the past, so a simple classification of individual frames might not be enough for accurate action recognition. Instead, I suggest training a CNN-LSTM-Attention model.
  
  => CNN will help capture spatial features.
  
  => LSTM (Long Short-Term Memory) will account for temporal dependencies (previous movements).
  
  => The attention mechanism can further enhance the model’s ability to focus on the most important frames/key points for classification.
  
  This architecture would allow you to classify actions based on sequences of key points extracted from videos, giving you much better accuracy than classifying single frames or individual poses.
  
  
  ---
  
  ### Answer by username_2 on 2019-12-27. Score: 0
  
  My suggestion is to go with 1st option. reason is you will get to know much about data and initial stage will find some challenges in developing the model, over a period of time you will get to better results after hypertunning. Please go through [article](http:////https://machinelearningmastery.com/deep-learning-models-for-human-activity-recognition/) , ignore you have already read this article
  
  > **Comments:**
  > **username_0**: Thanks for the article will read this. Any idea how I get a lot of images or how many I actually need?  
  > **username_2**: Are you looking for label data with images or just images, have found one data set, [link](http://vision.stanford.edu/Datasets/40actions.html).  
  > **username_0**: Thank you. For me it doesn't matter I can label them myself. Like for example pictures of people bending their arms. When searching for this on google not a lot of pictures show up. In the database you shared per action 280 pictures exist.  
  
  ---
  
  