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Human poses and motions are important cues for analysis of videos with peopleand there is strong evidence that representations based on body pose are highlyeffective for a variety of tasks such as activity recognition, contentretrieval and social signal processing. In this work, we aim to further advancethe state of the art by establishing "PoseTrack", a new large-scale benchmarkfor video-based human pose estimation and articulated tracking, and bringingtogether the community of researchers working on visual human analysis. Thebenchmark encompasses three competition tracks focusing on i) single-framemulti-person pose estimation, ii) multi-person pose estimation in videos, andiii) multi-person articulated tracking. To facilitate the benchmark andchallenge we collect, annotate and release a new %large-scale benchmark datasetthat features videos with multiple people labeled with person tracks andarticulated pose. A centralized evaluation server is provided to allowparticipants to evaluate on a held-out test set. We envision that the proposedbenchmark will stimulate productive research both by providing a large andrepresentative training dataset as well as providing a platform to objectivelyevaluate and compare the proposed methods. The benchmark is freely accessibleat https://posetrack.net.

PoseTrack: A Benchmark for Human Pose Estimation and Tracking