I have tried asking this on another forum, but after 4 months there are no posted answers, so I am asking it here:

What is the current state of the art approach for determining the pose of a person (including close poses like a head shot, and hand shot, etc...), and for segmenting the person from the background?

The setting here is single still images.


First of all, speaking about pose estimation I would remember Kinect sensor from Microsoft Research. They use depth-map, but the approach seems to be general: generate a HUGE amount of synthetic data and use it to train a HUGE Random Forest pixel-classifier. And do it for different body parts independently. For just still images you will use different features, including HOG as well. For further information have a look here: http://research.microsoft.com/en-us/projects/vrkinect/

The second thing that I want to mention is the research of the group of Vittorio Ferrari: http://groups.inf.ed.ac.uk/calvin/publications.html . They do a lot of stuff for body detection and pose estimation. The basic idea is to build an appearance model of the body. As of particular interest for your problem there is a paper: http://groups.inf.ed.ac.uk/calvin/Publications/eichner12ijcv.pdf . I would pay some attention to how they use spatio-temporal features. And of course it is always good to check references.

Hope it will help you to start. If I'll remember some different approaches - I will update the answer.


I was reading about "Histograms of Oriented Gradients for Human Detection" according to Dalal, there are a lot of derivative works, I guess is very close to what you are looking for.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.