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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;

/persons/resource/persons134279

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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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: https://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.