Pose Flow: Efficient Online Pose Tracking
Abstract
Multi-person articulated pose tracking in unconstrained videos is an important while challenging problem. In this paper, going along the road of top-down approaches, we propose a decent and efficient pose tracker based on pose flows. First, we design an online optimization framework to build the association of cross-frame poses and form pose flows (PF-Builder). Second, a novel pose flow non-maximum suppression (PF-NMS) is designed to robustly reduce redundant pose flows and re-link temporal disjoint ones. Extensive experiments show that our method significantly outperforms best-reported results on two standard Pose Tracking datasets by 13 mAP 25 MOTA and 6 mAP 3 MOTA respectively. Moreover, in the case of working on detected poses in individual frames, the extra computation of pose tracker is very minor, guaranteeing online 10FPS tracking. Our source codes are made publicly available(https://github.com/YuliangXiu/PoseFlow).
Cite
@article{arxiv.1802.00977,
title = {Pose Flow: Efficient Online Pose Tracking},
author = {Yuliang Xiu and Jiefeng Li and Haoyu Wang and Yinghong Fang and Cewu Lu},
journal= {arXiv preprint arXiv:1802.00977},
year = {2018}
}
Comments
Our source codes and models are made publicly available at https://github.com/YuliangXiu/PoseFlow and https://github.com/MVIG-SJTU/AlphaPose