VILSS: Upper body pose estimation for sign language and gesture recognition

James Charles, University of Leeds

In this talk I present methods for estimating the upper body pose of people performing gestures and sign language in long video sequences. Our methods are based on random forests classifiers and regressors which have proved successful for inferring pose from depth data (Kinect). Here, I will show how we develop methods to: (1) achieve real-time 2D upper body pose estimation without depth data, (2) produce structured pose output from a mixture of random forest experts, (3) use more image context while keeping the learning problem tractable and (4) incorporate temporal context using dense optical flow.