SoccerNet 2023 challenges results
The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high...
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| Veröffentlicht in: | Sports engineering Jg. 27; H. 2 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Heidelberg
Springer Nature B.V
01.12.2024
Springer Verlag |
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| ISSN: | 1369-7072, 1460-2687, 1460-2687 |
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| Abstract | The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related to describing events occurring in the video broadcasts: (1) action spotting, focusing on retrieving all timestamps related to global actions in soccer, (2) ball action spotting, focusing on retrieving all timestamps related to the soccer ball change of state, and (3) dense video captioning, focusing on describing the broadcast with natural language and anchored timestamps. The second theme, field understanding, relates to the single task of (4) camera calibration, focusing on retrieving the intrinsic and extrinsic camera parameters from images. The third and last theme, player understanding, is composed of three low-level tasks related to extracting information about the players: (5) re-identification, focusing on retrieving the same players across multiple views, (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams, and (7) jersey number recognition, focusing on recognizing the jersey number of players from tracklets. Compared to the previous editions of the SoccerNet challenges, tasks (2-3-7) are novel, including new annotations and data, task (4) was enhanced with more data and annotations, and task (6) now focuses on end-to-end approaches. Our report indicates performance trends across tasks: (1) Action spotting is nearing saturation, while (2) ball action spotting improved significantly with advanced end-to-end models. (3) Dense video captioning also saw substantial enhancements aligned with Large Language Models advancements. (4) Camera calibration, redefined end-to-end, demonstrated a significant performance boost. In contrast, (5) player re-identification showed only minor improvements, reflecting decreasing interest. The new (6) multiple object tracking task exhibited notable advances, underscoring the maturity of current techniques. (7) Jersey number recognition received the most focus, achieving impressive results. More information on the tasks, challenges, and leaderboards are available on https://www.soccer-net.org. Baselines and development kits can be found on https://github.com/SoccerNet. |
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| AbstractList | The SoccerNet 2023 challenges were the third annual video understanding
challenges organized by the SoccerNet team. For this third edition, the
challenges were composed of seven vision-based tasks split into three main
themes. The first theme, broadcast video understanding, is composed of three
high-level tasks related to describing events occurring in the video
broadcasts: (1) action spotting, focusing on retrieving all timestamps related
to global actions in soccer, (2) ball action spotting, focusing on retrieving
all timestamps related to the soccer ball change of state, and (3) dense video
captioning, focusing on describing the broadcast with natural language and
anchored timestamps. The second theme, field understanding, relates to the
single task of (4) camera calibration, focusing on retrieving the intrinsic and
extrinsic camera parameters from images. The third and last theme, player
understanding, is composed of three low-level tasks related to extracting
information about the players: (5) re-identification, focusing on retrieving
the same players across multiple views, (6) multiple object tracking, focusing
on tracking players and the ball through unedited video streams, and (7) jersey
number recognition, focusing on recognizing the jersey number of players from
tracklets. Compared to the previous editions of the SoccerNet challenges, tasks
(2-3-7) are novel, including new annotations and data, task (4) was enhanced
with more data and annotations, and task (6) now focuses on end-to-end
approaches. More information on the tasks, challenges, and leaderboards are
available on https://www.soccer-net.org. Baselines and development kits can be
found on https://github.com/SoccerNet. The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related to describing events occurring in the video broadcasts: (1) action spotting, focusing on retrieving all timestamps related to global actions in soccer, (2) ball action spotting, focusing on retrieving all timestamps related to the soccer ball change of state, and (3) dense video captioning, focusing on describing the broadcast with natural language and anchored timestamps. The second theme, field understanding, relates to the single task of (4) camera calibration, focusing on retrieving the intrinsic and extrinsic camera parameters from images. The third and last theme, player understanding, is composed of three low-level tasks related to extracting information about the players: (5) re-identification, focusing on retrieving the same players across multiple views, (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams, and (7) jersey number recognition, focusing on recognizing the jersey number of players from tracklets. Compared to the previous editions of the SoccerNet challenges, tasks (2-3-7) are novel, including new annotations and data, task (4) was enhanced with more data and annotations, and task (6) now focuses on end-to-end approaches. Our report indicates performance trends across tasks: (1) Action spotting is nearing saturation, while (2) ball action spotting improved significantly with advanced end-to-end models. (3) Dense video captioning also saw substantial enhancements aligned with Large Language Models advancements. (4) Camera calibration, redefined end-to-end, demonstrated a significant performance boost. In contrast, (5) player re-identification showed only minor improvements, reflecting decreasing interest. The new (6) multiple object tracking task exhibited notable advances, underscoring the maturity of current techniques. (7) Jersey number recognition received the most focus, achieving impressive results. More information on the tasks, challenges, and leaderboards are available on https://www.soccer-net.org. Baselines and development kits can be found on https://github.com/SoccerNet. |
| ArticleNumber | 24 |
| Author | Deuser, Fabian Baikulov, Ruslan Ma, Lin Song, Ran Zhang, Kexin Luo, Weixin Zhu, Yongqiang Liu, Bin Yan, Feng Chen, Chen Moeslund, Thomas B. Be’ery, Ishay Gan, Yiyang Fahrudin, Hasby Orcesi, Astrid Miralles, Pierre De Vleeschouwer, Christophe Zhou, Xin Fukushima, Ryuto Choi, Gyusik Cioppa, Anthony Kobayashi, Kenji Kim, Hankyul Alahi, Alexandre Hérault, Romain Synowiec, Kamil Someya, Taiga Chen, Ruilong Uchida, Ikuma Zeng, Yingsen Ghanem, Bernard Xarles, Artur Nang, Jongho Muhammad, Iftikar Oswald, Norbert Somers, Vladimir Mansourian, Amir M. Jia, Qiong Ding, Shouhong Chen, Shimin Zhang, Junpei Barnich, Olivier Magera, Floriane Falaleev, Nikolay Ma, Yanbiao Mkhallati, Hassan Liashuha, Mykola Giancola, Silvio Liu, Ruixuan Salah, Ibrahim Li, Junjie Guo, Hao Held, Jan Shen, Wei Lee, Jeongae Lee, Seungcheon Hinojosa, Carlos Deliège, Adrien Zhang, Wenjie Yerushalmy, Ido Nasr, Mohamed Denize, Julien Li, Zhiheng Yang, Xinquan Abdelaziz, Amr Van Droogenbroeck, Marc Zhao, Wending Wang, Guanshuo Xu, Jinghang Peng, Rui Zhong, Yujie Li, Wei Ardö, Håkan Maglo, Adrien Li, T |
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| Snippet | The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were... The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were... |
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| SubjectTerms | Annotations Calibration Cameras Computer Science - Artificial Intelligence Computer Science - Computer Vision and Pattern Recognition Electrical & electronics engineering Engineering, computing & technology Ingénierie électrique & électronique Ingénierie, informatique & technologie Large language models Multiple target tracking Object recognition Parameter identification Players Soccer Video broadcasting Video data |
| Title | SoccerNet 2023 challenges results |
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