@online{Doering_arXiv2011.06243,
TITLE = {{PoseTrackReID}: {D}ataset Description},
AUTHOR = {Doering, Andreas and Chen, Di and Zhang, Shanshan and Schiele, Bernt and Gall, Juergen},
LANGUAGE = {eng},
URL = {https://arxiv.org/abs/2011.06243},
EPRINT = {2011.06243},
EPRINTTYPE = {arXiv},
YEAR = {2020},
ABSTRACT = {Current datasets for video-based person re-identification (re-ID) do not<br>include structural knowledge in form of human pose annotations for the persons<br>of interest. Nonetheless, pose information is very helpful to disentangle<br>useful feature information from background or occlusion noise. Especially<br>real-world scenarios, such as surveillance, contain a lot of occlusions in<br>human crowds or by obstacles. On the other hand, video-based person re-ID can<br>benefit other tasks such as multi-person pose tracking in terms of robust<br>feature matching. For that reason, we present PoseTrackReID, a large-scale<br>dataset for multi-person pose tracking and video-based person re-ID. With<br>PoseTrackReID, we want to bridge the gap between person re-ID and multi-person<br>pose tracking. Additionally, this dataset provides a good benchmark for current<br>state-of-the-art methods on multi-frame person re-ID.<br>},
}