Answers.Body: <p>You can use any state of the art object detection such as DETR, EfficientDET, YOLO etc. since they have an object 'person' in them. But, if the density of people is high in the images, then, you should look at '<a href="https://paperswithcode.com/paper/finding-tiny-faces#" rel="nofollow noreferrer">Finding Tiny Faces</a>' which is good for that problem (this will help you detect faces only, think group photos). If you want to find the whole person even if they are occluded, then, it will be difficult (you can then think of segmentation techniques and then, try to find out the people but it will be more taxing).</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-02-25T19:55:11.377Z
Answers.LastActivityDate: 2021-02-25T19:55:11.377Z
Answers.UserDisplayName: Abhishek Verma
Body: <p>I would like to use a deep learning approach to detect people in videos. I have found some freely accessible implementations like Human Segementation with Pytorch or BodyPix / DeepLab / Pixellib with Tensorflow. They all work well, but with many it happens that, for example, half hand is not detected or if a person is sitting in the picture only the legs and the head are detected. Are there other approaches to detect people who are freely accessible or is that state-of-the-art?
  I had imagined such problems have been solved, but I don't know so much about it. Thanks for your answers.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-02-26T12:51:58.480Z
Comments.Text: Person detection and human segmentation should not be the same things. So, please, edit your post to clarify if you're looking for one or the other. Moreover, it may be a good idea to provide the links to the implementations/models you have found so far. In any case, note that asking for "software libraries" is off-topic. If you're looking for references (e.g. papers), that's ok, but clarify that in your post, please.
Comments.UserDisplayName: nbro
Tags: <convolutional-neural-networks><object-detection><image-segmentation><semantic-segmentation>
Title: What are the state-of-the-art Person-Detektion / Human-Segmentation?
UserDisplayName: miimi
ThreadText: # What are the state-of-the-art Person-Detektion / Human-Segmentation?
  
  (Asked by: username_0 on 2021-02-25)
  
  I would like to use a deep learning approach to detect people in videos. I have found some freely accessible implementations like Human Segementation with Pytorch or BodyPix / DeepLab / Pixellib with Tensorflow. They all work well, but with many it happens that, for example, half hand is not detected or if a person is sitting in the picture only the legs and the head are detected. Are there other approaches to detect people who are freely accessible or is that state-of-the-art?
  I had imagined such problems have been solved, but I don't know so much about it. Thanks for your answers.
  
  **Tags:** convolutional-neural-networks, object-detection, image-segmentation, semantic-segmentation
  > **Comments:**
  > **username_1**: Person detection and human segmentation should not be the same things. So, please, edit your post to clarify if you're looking for one or the other. Moreover, it may be a good idea to provide the links to the implementations/models you have found so far. In any case, note that asking for "software libraries" is off-topic. If you're looking for references (e.g. papers), that's ok, but clarify that in your post, please.  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2021-02-25. Score: 0
  
  You can use any state of the art object detection such as DETR, EfficientDET, YOLO etc. since they have an object 'person' in them. But, if the density of people is high in the images, then, you should look at '[Finding Tiny Faces](https://paperswithcode.com/paper/finding-tiny-faces#)' which is good for that problem (this will help you detect faces only, think group photos). If you want to find the whole person even if they are occluded, then, it will be difficult (you can then think of segmentation techniques and then, try to find out the people but it will be more taxing).
  
  
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