The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team. In 2022, the challenges were composed of 6 vision-based tasks: (1) action spotting, focusing on retrieving action timestamps in long untrimmed videos, (2) replay grounding, focusing on retrieving the live moment of an action shown in a replay, (3) pitch localization, focusing on detecting line and goal part elements, (4) camera calibration, dedicated to retrieving the intrinsic and extrinsic camera parameters, (5) player re-identification, focusing on retrieving the same players across multiple views, and (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams. Compared to last year's challenges, tasks (1-2) had their evaluation metrics redefined to consider tighter temporal accuracies, and tasks (3-6) were novel, including their underlying data and annotations. More information on the tasks, challenges and leaderboards are available on https://www.soccer-net.org. Baselines and development kits are available on https://github.com/SoccerNet.
|Title of host publication||MMSports 2022 - Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports|
|Number of pages||12|
|Publisher||Association for Computing Machinery|
|Publication date||14 Oct 2022|
|Publication status||Published - 14 Oct 2022|
|Event||5th ACM International Workshop on Multimedia Content Analysis in Sports, MMSports 2022, co-located with ACM Multimedia 2022 - Lisboa, Portugal|
Duration: 14 Oct 2022 → …
|Conference||5th ACM International Workshop on Multimedia Content Analysis in Sports, MMSports 2022, co-located with ACM Multimedia 2022|
|Period||14/10/2022 → …|
|Series||MMSports 2022 - Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports|
Bibliographical noteFunding Information:
This work was supported by the Service Public de Wallonie (SPW) Recherche under the DeepSport project and Grant No. 2010235 (ARIAC by https://DigitalWallonia4.ai), the FRIA, the FNRS, and KAUST Oce of Sponsored Research through the Visual Computing Center funding.
© 2022 Owner/Author.
- computer vision
- neural networks
- video understanding