Skip to Main content Skip to Navigation
New interface
Conference papers

Efficient tracking of team sport players with few game-specific annotations

Abstract : One of the requirements for team sports analysis is to track and recognize players. Many tracking and reidentification methods have been proposed in the context of video surveillance. They show very convincing results when tested on public datasets such as the MOT challenge. However, the performance of these methods are not as satisfactory when applied to player tracking. Indeed, in addition to moving very quickly and often being occluded, the players wear the same jersey, which makes the task of reidentification very complex. Some recent tracking methods have been developed more specifically for the team sport context. Due to the lack of public data, these methods use private datasets that make impossible a comparison with them. In this paper, we propose a new generic method to track team sport players during a full game thanks to few human annotations collected via a semi-interactive system. Non-ambiguous tracklets and their appearance features are automatically generated with a detection and a reidentification network both pre-trained on public datasets. Then an incremental learning mechanism trains a Transformer to classify identities using few game-specific human annotations. Finally, tracklets are linked by an association algorithm. We demonstrate the efficiency of our approach on a challenging rugby sevens dataset. To overcome the lack of public sports tracking dataset, we publicly release this dataset at https://kalisteo.cea.fr/index.php/free-resources/. We also show that our method is able to track rugby sevens players during a full match, if they are observable at a minimal resolution, with the annotation of only 6 few seconds length tracklets per player.
Complete list of metadata

https://hal-cea.archives-ouvertes.fr/cea-03789225
Contributor : Contributeur MAP CEA Connect in order to contact the contributor
Submitted on : Friday, September 30, 2022 - 9:31:30 AM
Last modification on : Sunday, October 2, 2022 - 3:24:58 AM

File

 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2023-03-26

Please log in to resquest access to the document

Identifiers

Citation

Adrien Maglo, Astrid Orcesi, Quoc-Cuong Pham. Efficient tracking of team sport players with few game-specific annotations. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE, Jun 2022, New orleans, United States. pp.3461-3471, ⟨10.1109/CVPRW56347.2022.00390⟩. ⟨cea-03789225⟩

Share

Metrics

Record views

29