Real-time upper body pose estimation from depth images

Estimating upper body poses from a sequence of depth images is a challenging problem. Lately, the state-of-art work adopted a randomized forest method to label human parts in real time. However, it requires enormous training data to obtain favorable results. In this paper, we propose using a novel two-stage method to estimate the probability maps of upper body parts of users. These maps are then used to evaluate the region fitness of body parts for pose recovery. Experiments show that the proposed method can obtain satisfactory outcome in real time and it requires a moderate size of training data.