TY - GEN
T1 - SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos
AU - Cioppa, Anthony
AU - Giancola, Silvio
AU - Deliège, Adrien
AU - Kang, Le
AU - Zhou, Xin
AU - Cheng, Zhiyu
AU - Ghanem, Bernard
AU - Droogenbroeck, Marc Van
N1 - KAUST Repository Item: Exported on 2022-09-14
Acknowledged KAUST grant number(s): CRG2017-3405
Acknowledgements: This work was supported by the Service Public de Wallonie (SPW) Recherche, under Grant No. 2010235 – ARIAC by DigitalWallonia4.ai, and KAUST Office of Sponsored Research (CRG2017-3405).
PY - 2022/8/23
Y1 - 2022/8/23
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/676620
UR - https://ieeexplore.ieee.org/document/9857224/
U2 - 10.1109/CVPRW56347.2022.00393
DO - 10.1109/CVPRW56347.2022.00393
M3 - Conference contribution
SN - 978-1-6654-8740-5
BT - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
PB - IEEE
ER -