Papers › ArtTrack: Articulated Multi-person Tracking in the Wild

ArtTrack: Articulated Multi-person Tracking in the Wild

5 Dec 2016CVPR 2017 7arXiv:1612.01465archive 2025-07-28

Eldar Insafutdinov, Mykhaylo Andriluka, Leonid Pishchulin, Siyu Tang, Evgeny Levinkov, Bjoern Andres, Bernt Schiele

In this paper we propose an approach for articulated tracking of multiple people in unconstrained videos. Our starting point is a model that resembles existing architectures for single-frame pose estimation but is substantially faster. We achieve this in two ways: (1) by simplifying and sparsifying the body-part relationship graph and leveraging recent methods for faster inference, and (2) by offloading a substantial share of computation onto a feed-forward convolutional architecture that is able to detect and associate body joints of the same person even in clutter. We use this model to generate proposals for body joint locations and formulate articulated tracking as spatio-temporal grouping of such proposals. This allows to jointly solve the association problem for all people in the scene by propagating evidence from strong detections through time and enforcing constraints that each proposal can be assigned to one person only. We report results on a public MPII Human Pose benchmark and on a new MPII Video Pose dataset of image sequences with multiple people. We demonstrate that our model achieves state-of-the-art results while using only a fraction of time and is able to leverage temporal information to improve state-of-the-art for crowded scenes.

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PJunhyuk/exercise-pose-analyzer mentioned on GitHubtfApache-2.0 report
PJunhyuk/people-counting-pose mentioned on GitHubtfApache-2.0 report
ayeshathanawalla/DeepLabCut mentioned on GitHubtfLGPL-3.0 report
eldar/pose-tensorflow mentioned on GitHubtfLGPL-3.0 report
estelabalboa/Proyecto_Final_Pilates mentioned on GitHubtfLGPL-3.0 report
janbertelngo/count-people mentioned on GitHubtfApache-2.0 report
ZJX9999/art_track mindsporeApache-2.0 report

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Tasks

Keypoint DetectionMulti-Person Pose EstimationPose Estimation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Keypoint Detection MPII Multi-Person Articulated Tracking mAP@0.5 74.3% #7 of 9 Archive leaderboard report
Multi-Person Pose Estimation MPII Multi-Person Articulated Tracking AP 74.3% #7 of 9 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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