مؤتمر
SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos
| العنوان: | SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos |
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| المؤلفون: | Cioppa, Anthony, Giancola, Silvio, Deliège, Adrien, Kang, Le, Zhou, Xin, Cheng, Zhiyu, Ghanem, Bernard, Van Droogenbroeck, Marc |
| المساهمون: | Montefiore Institute - Montefiore Institute of Electrical Engineering and Computer Science - ULiège, TELIM |
| المصدر: | IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 3490-3501 (2022-06); International Workshop on Computer Vision in Sports (CVsports), New Orleans, United States - Louisiana [US-LA], du 19 juin 2022 au 20 juin 2022 |
| بيانات النشر: | IEEE, 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Computer vision, Deep learning, SoccerNet-v3, Human tracking, Player tracking, Soccer, Sports, Sport, Football, Engineering, computing & technology, Electrical & electronics engineering, Ingénierie, informatique & technologie, Ingénierie électrique & électronique |
| الوصف: | 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. Applications et Recherche pour une Intelligence Artificielle de Confiance (ARIAC) |
| نوع الوثيقة: | conference paper http://purl.org/coar/resource_type/c_5794Test conferenceObject peer reviewed |
| اللغة: | English |
| DOI: | 10.1109/CVPRW56347.2022.00393 |
| الوصول الحر: | https://orbi.uliege.be/handle/2268/289685Test |
| حقوق: | open access http://purl.org/coar/access_right/c_abf2Test info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsorb.289685 |
| قاعدة البيانات: | ORBi |
| DOI: | 10.1109/CVPRW56347.2022.00393 |
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