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  PoseTrackReID: Dataset Description

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

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Latex : {PoseTrackReID}: {D}ataset Description

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arXiv:2011.06243.pdf (Preprint), 58KB
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arXiv:2011.06243.pdf
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 Creators:
Doering, Andreas1, Author
Chen, Di2, Author           
Zhang, Shanshan2, Author           
Schiele, Bernt2, Author           
Gall, Juergen1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society, ou_1116547              

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Free keywords: Computer Science, Computer Vision and Pattern Recognition, cs.CV
 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.

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Language(s): eng - English
 Dates: 2020-11-122020
 Publication Status: Published online
 Pages: 3 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: arXiv: 2011.06243
BibTex Citekey: Doering_arXiv2011.06243
URI: https://arxiv.org/abs/2011.06243
 Degree: -

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