Computer vision; Deep learning; SoccerNet-v3; Human tracking; Player tracking; Soccer; Sports; Sport; Football
Abstract :
[en] Tracking objects in soccer videos is extremely important to gather both player and team statistics, whether it is to estimate the total distance run, the ball possession or the team formation. Video processing can help automating the extraction of those information, without the need of any invasive sensor, hence applicable to any team on any stadium. Yet, the availability of datasets to train learnable models and benchmarks to evaluate methods on a common testbed is very limited. In this work, we propose a novel dataset for multiple object tracking composed of 200 sequences of 30s each, representative of challenging soccer scenarios, and a complete 45-minutes half-time for long-term tracking. The dataset is fully annotated with bounding boxes and tracklet IDs, enabling the training of MOT baselines in the soccer domain and a full benchmarking of those methods on our segregated challenge sets. Our analysis shows that multiple player, referee and ball tracking in soccer videos is far from being solved, with several improvement required in case of fast motion or in scenarios of severe occlusion.
Research center :
Montefiore Institute - Montefiore Institute of Electrical Engineering and Computer Science - ULiège TELIM
Disciplines :
Electrical & electronics engineering
Author, co-author :
Cioppa, Anthony ✱; Université de Liège - ULiège > Montefiore Institute of Electrical Engineering and Computer Science
Giancola, Silvio ✱
Deliège, Adrien ✱; Université de Liège - ULiège > Département d'électricité, électronique et informatique (Institut Montefiore) > Télécommunications
Kang, Le ✱
Zhou, Xin ✱
Cheng, Zhiyu
Ghanem, Bernard
Van Droogenbroeck, Marc ; Université de Liège - ULiège > Montefiore Institute of Electrical Engineering and Computer Science
✱ These authors have contributed equally to this work.
Language :
English
Title :
SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos
Publication date :
June 2022
Event name :
International Workshop on Computer Vision in Sports (CVsports)
Event organizer :
IEEE
Event place :
New Orleans, United States - Louisiana
Event date :
du 19 juin 2022 au 20 juin 2022
Audience :
International
Main work title :
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Publisher :
IEEE
Pages :
3490-3501
Peer reviewed :
Peer reviewed
Name of the research project :
Applications et Recherche pour une Intelligence Artificielle de Confiance (ARIAC)
Funders :
SPW - Public Service of Wallonia [BE]
Funding number :
2010235
Funding text :
This work was supported by the Service Public de Wallonie (SPW) Recherche, under Grant No. 2010235 – ARIAC by DigitalWallonia4.ai
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