Enhancing human pose estimation with temporal clues

We address the challenging problem of human pose estimation, which can be adopted as a preprocessing step providing accurate and refined humanpose information for gait recognition and other applications. In this paper, we propose a method and augmented Pose-NMS to process the human pose estimation in the consecutive frames based on a reasonable assumption. The poses between the adjacent frames have small changes. Firstly we merge the multiple estimated pose candidates in a single frame to get the representative pose candidates. Then we propagate the final candidate backward and forward to increase the number of the confident candidates based on the Bayesian theory. We apply our method to the Buffy Video dataset and obtain the competitive result to the state-of-art.