VisDrone-MOT2021: The Vision Meets Drone Multiple Object Tracking Challenge Results

Chen, G; Wang, W; He, Z; Wang, L; Yuan, Y; Zhang, D; Zhang, J; Zhu, P; Van Gool, L; Han, J; Hoi, S; Hu, Q; Liu, M; Sciarrone, A; Sun, C; Garibotto, C; Tran, DNN; Lavagetto, F; Haleem, H; Motorcu, H; Ates, HF; Nguyen, HH; Jeon, HJ; Bisio, I; Jeon, JW; Li, J; Pham, LH; Jeon, M; Feng, Q; Li, S; Tran, THP; Pan, X; Song, YM; Yao, Y; Du, Y; Xu, Z; Luo, Z

VisDrone-MOT2021: The Vision Meets Drone Multiple Object Tracking Challenge Results

Chen, G Wang, W He, Z Wang, L Yuan, Y Zhang, D Zhang, J Zhu, P Van Gool, L Han, J Hoi, S Hu, Q Liu, M Sciarrone, A Sun, C Garibotto, C Tran, DNN Lavagetto, F Haleem, H Motorcu, H Ates, HF Nguyen, HH Jeon, HJ Bisio, I Jeon, JW Li, J Pham, LH Jeon, M Feng, Q

Li, S Tran, THP Pan, X Song, YM Yao, Y Du, Y Xu, Z Luo, Z

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of the IEEE International Conference on Computer Vision, 2021, 2021-October, pp. 2839-2846
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Chen, G
dc.contributor.author	Wang, W
dc.contributor.author	He, Z
dc.contributor.author	Wang, L
dc.contributor.author	Yuan, Y
dc.contributor.author	Zhang, D
dc.contributor.author	Zhang, J
dc.contributor.author	Zhu, P
dc.contributor.author	Van Gool, L
dc.contributor.author	Han, J
dc.contributor.author	Hoi, S
dc.contributor.author	Hu, Q
dc.contributor.author	Liu, M
dc.contributor.author	Sciarrone, A
dc.contributor.author	Sun, C
dc.contributor.author	Garibotto, C
dc.contributor.author	Tran, DNN
dc.contributor.author	Lavagetto, F
dc.contributor.author	Haleem, H
dc.contributor.author	Motorcu, H
dc.contributor.author	Ates, HF
dc.contributor.author	Nguyen, HH
dc.contributor.author	Jeon, HJ
dc.contributor.author	Bisio, I
dc.contributor.author	Jeon, JW
dc.contributor.author	Li, J
dc.contributor.author	Pham, LH
dc.contributor.author	Jeon, M
dc.contributor.author	Feng, Q https://orcid.org/0000-0003-1014-6081
dc.contributor.author	Li, S
dc.contributor.author	Tran, THP
dc.contributor.author	Pan, X
dc.contributor.author	Song, YM
dc.contributor.author	Yao, Y
dc.contributor.author	Du, Y
dc.contributor.author	Xu, Z
dc.contributor.author	Luo, Z
dc.date	2021-10-11
dc.date.accessioned	2022-07-04T06:41:26Z
dc.date.available	2022-07-04T06:41:26Z
dc.date.issued	2021-01-01
dc.identifier.citation	Proceedings of the IEEE International Conference on Computer Vision, 2021, 2021-October, pp. 2839-2846
dc.identifier.isbn	9781665401913
dc.identifier.issn	1550-5499
dc.identifier.uri	http://hdl.handle.net/10453/158613
dc.description.abstract	Vision Meets Drone: Multiple Object Tracking (VisDrone-MOT2021) challenge - the forth annual activity organized by the VisDrone team - focuses on benchmarking UAV MOT algorithms in realistic challenging environments. It is held in conjunction with ICCV 2021. VisDrone-MOT2021 contains 96 video sequences in total, including 56 sequences (~24K frames) for training, 7 sequences (~3K frames) for validation and 33 sequences (~13K frames) for testing. Bounding-box annotations for novel object categories are provided every frame and temporally consistent instance IDs are also given. Additionally, occlusion ratio and truncation ratio are provided as extra useful annotations. The results of eight state-of-the-art MOT algorithms are reported and discussed. We hope that our VisDrone-MOT2021 challenge will facilitate future research and applications in the field of UAV vision. The website of our challenge can be found at http://www.aiskyeye.com/.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings of the IEEE International Conference on Computer Vision
dc.relation.ispartof	IEEE/CVF International Conference on Computer Vision Workshops
dc.relation.isbasedon	10.1109/ICCVW54120.2021.00318
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	VisDrone-MOT2021: The Vision Meets Drone Multiple Object Tracking Challenge Results
dc.type	Conference Proceeding
utslib.citation.volume	2021-October
utslib.location.activity	Montreal, BC, Canada
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-07-04T06:41:25Z
pubs.finish-date	2021-10-17
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2021-10-11
pubs.volume	2021-October
dc.location	Piscataway, USA

Abstract:

Vision Meets Drone: Multiple Object Tracking (VisDrone-MOT2021) challenge - the forth annual activity organized by the VisDrone team - focuses on benchmarking UAV MOT algorithms in realistic challenging environments. It is held in conjunction with ICCV 2021. VisDrone-MOT2021 contains 96 video sequences in total, including 56 sequences (~24K frames) for training, 7 sequences (~3K frames) for validation and 33 sequences (~13K frames) for testing. Bounding-box annotations for novel object categories are provided every frame and temporally consistent instance IDs are also given. Additionally, occlusion ratio and truncation ratio are provided as extra useful annotations. The results of eight state-of-the-art MOT algorithms are reported and discussed. We hope that our VisDrone-MOT2021 challenge will facilitate future research and applications in the field of UAV vision. The website of our challenge can be found at http://www.aiskyeye.com/.

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http://hdl.handle.net/10453/158613