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PoseTrackReID: Dataset Description

MPS-Authors
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Chen,  Di
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

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Zhang,  Shanshan
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

/persons/resource/persons45383

Schiele,  Bernt
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

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Fulltext (public)

arXiv:2011.06243.pdf
(Preprint), 58KB

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Citation

Doering, A., Chen, D., Zhang, S., Schiele, B., & Gall, J. (2020). PoseTrackReID: Dataset Description. Retrieved from https://arxiv.org/abs/2011.06243.


Cite as: http://hdl.handle.net/21.11116/0000-0007-80FA-E
Abstract
Current datasets for video-based person re-identification (re-ID) do not include structural knowledge in form of human pose annotations for the persons of interest. Nonetheless, pose information is very helpful to disentangle useful feature information from background or occlusion noise. Especially real-world scenarios, such as surveillance, contain a lot of occlusions in human crowds or by obstacles. On the other hand, video-based person re-ID can benefit other tasks such as multi-person pose tracking in terms of robust feature matching. For that reason, we present PoseTrackReID, a large-scale dataset for multi-person pose tracking and video-based person re-ID. With PoseTrackReID, we want to bridge the gap between person re-ID and multi-person pose tracking. Additionally, this dataset provides a good benchmark for current state-of-the-art methods on multi-frame person re-ID.