Papers › Pose Flow: Efficient Online Pose Tracking

Pose Flow: Efficient Online Pose Tracking

3 Feb 2018arXiv:1802.00977archive 2025-07-28

Yuliang Xiu, Jiefeng Li, Haoyu Wang, Yinghong Fang, Cewu Lu

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).

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Tasks

Pose Tracking

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Keypoint Detection COCO test-challenge Xiu et al. AR 67.5 #8 of 8 Archive leaderboard report
Keypoint Detection COCO test-challenge Xiu et al. ARM 62.5 #8 of 8 Archive leaderboard report
Pose Tracking PoseTrack2017 PoseFlow MOTA 50.98 #9 of 10 Archive leaderboard report
Pose Tracking PoseTrack2017 PoseFlow mAP 62.95 #9 of 10 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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