Multi-person Pose Estimation for Pose Tracking with Enhanced Cascaded Pyramid Network

Multi-person pose estimation is a fundamental yet challenging task in machine learning. In parallel, recent development of pose estimation has increased interests on pose tracking in recent years. In this work, we propose an efficient and powerful method to locate and track human pose. Our proposed method builds upon the state-of-the-art single person pose estimation system (Cascaded Pyramid Network), and adopts the IOU-tracker module to identify the people in the wild. We conduct experiments on the released multi-person video pose estimation benchmark (PoseTrack2018) to validate the effectiveness of our network. Our model achieves an accuracy of 80.9% on the validation and 77.1% on the test set using the Mean Average Precision (MAP) metric, an accuracy of 64.0% on the validation and 57.4% on the test set using the Multi-Object Tracking Accuracy (MOTA) metric.