A Medium Granularity Model for Human Pose Estimation in Video

Human pose estimation has attracted much attention in the computer vision community due to its potential applications in action recognition, human-computer interaction, etc. To focus on pose estimation in videos, a medium granularity spatio-temporal probabilistic graphical model using body part tracklets as entities is presented in this paper. The optimal tracklet for each body part is acquired by spatiotemporal approximate reasoning through iterative spatial and temporal parsing, and the final human pose estimation is achieved by merging these optimal tracklets. To generate reliable tracklet proposals, global motion cue is adopted to propagate pose detections from individual frames to the whole video, and the trajectories from this propagation are segmented into fixed-length overlapping tracklets. To deal with the double counting problem, symmetric parts are coupled to one virtual node, so that the loops in spatial model are removed and the constaints between symmetric parts are maintained. The experiment on three datasets shows the proposed method achieves a higher accuracy than other pose estimation methods.