15 Keypoints Is All You Need
Pose tracking is an important problem that requires identifying unique human pose instances and matching them temporally across different frames in a video. However, existing pose tracking methods are unable to accurately model temporal relationships and require significant computation, often computing the tracks offline. We present an efficient multi person pose tracking method, KeyTrack that only relies on keypoint information without using any RGB or optical flow to locate and track human keypoints in real time. KeyTrack is a top down approach that learns spatio temporal pose relationships by modeling the multi person pose tracking problem as a novel Pose Entailment task using a Transformer based architecture. Furthermore, KeyTrack uses a novel, parameter free, keypoint refinement technique that improves the keypoint estimates used by the Transformers. We achieve state of the art results on PoseTrack'17 and PoseTrack'18 benchmarks while using only a fraction of the computation used by most other methods for computing the tracking information.
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)