15 Keypoints Is All You Need
This addresses the need for efficient and accurate pose tracking in computer vision applications, though it appears incremental as it builds on existing pose estimation and tracking frameworks.
The paper tackles the problem of multi-person pose tracking in videos by developing KeyTrack, a method that uses only keypoint information without RGB or optical flow to track human poses in real-time. It achieves state-of-the-art results on PoseTrack'17 and PoseTrack'18 benchmarks while requiring significantly less computation than existing methods.
Pose tracking is an important problem that requires identifying unique human pose-instances and matching them temporally across different frames of 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 information to track human keypoints in real-time. Keypoints are tracked using our Pose Entailment method, in which, first, a pair of pose estimates is sampled from different frames in a video and tokenized. Then, a Transformer-based network makes a binary classification as to whether one pose temporally follows another. Furthermore, we improve our top-down pose estimation method with a novel, parameter-free, keypoint refinement technique that improves the keypoint estimates used during the Pose Entailment step. We achieve state-of-the-art results on the PoseTrack'17 and the PoseTrack'18 benchmarks while using only a fraction of the computation required by most other methods for computing the tracking information.