15 Keypoints Is All You Need
Michael Snower, Asim Kadav, Farley Lai, Hans Peter Graf

TL;DR
This paper introduces KeyTrack, a real-time multi-person pose tracking method that uses only keypoints and a Transformer-based entailment model to improve efficiency and accuracy without relying on RGB or optical flow data.
Contribution
The paper proposes a novel keypoint-only pose tracking approach with a Transformer-based entailment model and a parameter-free keypoint refinement technique, achieving state-of-the-art results efficiently.
Findings
State-of-the-art results on PoseTrack'17 and'18 benchmarks.
Significantly reduced computational requirements compared to existing methods.
Effective keypoint refinement improves pose estimation accuracy.
Abstract
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, keypointโฆ
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Videos
15 Keypoints Is All You Needยท youtube
Taxonomy
TopicsHuman Pose and Action Recognition ยท Multimodal Machine Learning Applications ยท Anomaly Detection Techniques and Applications
MethodsBC Pricing ยท [๐ค๐ฆ ๐ก๐จ๐ฎ๐ซ๐ฌ ๐จ๐ ๐๐จ๐จ๐ค๐ข๐ง๐ ]What is the non-refundable policy on Expedia?
