Video Pose Track with Graph-Guided Sparse Motion Estimation

—In this paper, we propose a novel framework for multi-person pose estimation and tracking under occlusions and motion blurs. Specifically, the consistency in graph structures from consecutive frames is improved by concentrating on visible body joints and estimating the motion vectors of sparse key-points surrounding visible joints. The proposed framework involves three components: (i) A Sparse Key-point Flow Estimating Module (SKFEM) for sampling key-points from around body joints and estimating the motion vectors of key-points which contribute to the refinement of body joint locations and fine-tuning of pose estimators; (ii) A Hierarchical Graph Distance Minimizing Module (HGMM) for evaluating the visibility scores of nodes from hierarchical graphs with the visibility score of a node determining the number of samples around that node; and (iii) The combination of multiple historical frames for matching identities. Graph matching with HGMM facilitates more accurate tracking even under partial occlusions. The proposed approach not only achieves state-of-the-art performance on the PoseTrack dataset but also contributes to significant improvements in human-related anomaly detection. Besides a higher accuracy, the proposed SKFEM also shows a much higher efficiency than dense optical flow estimation.