Accurate whole-body multi-person pose estimation and tracking is an important yet challenging topic in computer vision. 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. In this paper, we present AlphaPose, a system that can perform accurate whole-body pose estimation and tracking jointly while running in realtime. 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. During training, we resort to Part-Guided Proposal Generator (PGPG) and multi-domain knowledge distillation to further improve the accuracy. Our method is able to localize whole-body keypoints accurately and tracks humans simultaneously given inaccurate bounding boxes and redundant detections. We show a significant improvement over current state-of-the-art methods in both speed and accuracy on COCO-wholebody, COCO, PoseTrack, and our proposed Halpe-FullBody pose estimation dataset. Our model, source codes and dataset are made publicly available at https://github.com/MVIG-SJTU/AlphaPose.

本文介绍了一个实时运行的系统AlphaPose，它可以同时执行准确的全身姿势估计和跟踪。我们使用了几种新技术，包括对称积分关键点回归（SIKR）用于快速和精确的定位，参数化姿势非最大抑制（P-NMS）用于消除冗余的人体检测和姿势感知身份嵌入，用于联合姿势估计和跟踪。我们的方法能够准确地定位全身关键点并同时跟踪人，可以显著提高COCO-wholebody，COCO，PoseTrack和我们提出的Halpe-FullBody姿势估计数据集的速度和准确性。

AlphaPose: 实时多人全身区域姿态估计和跟踪