Improving Multi-Person Pose Tracking with A Confidence Network
Zehua Fu Wenhang Zuo Zhenghui Hu Qingjie Liu Yunhong Wang
Human pose estimation and tracking are fundamental tasks for understanding
human behaviors in videos. Existing top-down framework-based methods usually
perform three-stage tasks: human detection, pose estimation and tracking.
Although promising results have been achieved, these methods rely heavily on
high-performance detectors and may fail to track persons who are occluded or
miss-detected. To overcome these problems, in this paper, we develop a novel
keypoint confidence network and a tracking pipeline to improve human detection
and pose estimation in top-down approaches. Specifically, the keypoint
confidence network is designed to determine whether each keypoint is occluded,
and it is incorporated into the pose estimation module. In the tracking
pipeline, we propose the Bbox-revision module to reduce missing detection and
the ID-retrieve module to correct lost trajectories, improving the performance
of the detection stage. Experimental results show that our approach is
universal in human detection and pose estimation, achieving state-of-the-art
performance on both PoseTrack 2017 and 2018 datasets.