AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time

Accurate whole-body multi-person pose estimation and tracking is an important yet challenging topic in computer vision. We show a significant improvement over current advanced methods in both speed and accuracy on COCO-wholebody, COCO, PoseTrack, and our proposed Halpe-FullBody pose estimation dataset.

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• 1 To capture the subtle actions of humans for complex behavior analysis, whole-body pose estimation including the face, body, hand and foot is essential over conventional body-only pose estimation.
• 2 In this article, we present AlphaPose, a system that can perform accurate whole-body pose estimation and tracking jointly while running in realtime.
• 3 To this end, we propose several new techniques: Symmetric Integral Keypoint Regression (SIKR) for fast and fine localization, Parametric Pose Non-Maximum-Suppression (P-NMS) for eliminating redundant human detections and Pose Aware Identity Embedding for jointly pose estimation and tracking.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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