Video pose estimation via medium granularity graphical model with spatial-temporal symmetric constraint part model

We address the problem of full body human pose estimation in video. Most previous work consider body part, pose or trajectory of body part as basic unit to compose the pose sequence. In contrast, we consider tracklet of body part as the basic unit. Based on this medium granularity representation we develop a spatio-temporal graphical model to select an optimal tracklet for each part in each video segment. In our model, tracklet nodes of symmetric parts are coupled to one node to overcome the double counting problem. Through iterative spatial and temporal parsing, optimal solution is achieved in polynomial time. We apply our model on three publicly available datasets and show remarkable quantitative and qualitative improvements over the state-of-the-art approaches.