SoccerNet 2024 Challenges Results
- URL: http://arxiv.org/abs/2409.10587v1
- Date: Mon, 16 Sep 2024 14:12:22 GMT
- Title: SoccerNet 2024 Challenges Results
- Authors: Anthony Cioppa, Silvio Giancola, Vladimir Somers, Victor Joos, Floriane Magera, Jan Held, Seyed Abolfazl Ghasemzadeh, Xin Zhou, Karolina Seweryn, Mateusz Kowalczyk, Zuzanna Mróz, Szymon Łukasik, Michał Hałoń, Hassan Mkhallati, Adrien Deliège, Carlos Hinojosa, Karen Sanchez, Amir M. Mansourian, Pierre Miralles, Olivier Barnich, Christophe De Vleeschouwer, Alexandre Alahi, Bernard Ghanem, Marc Van Droogenbroeck, Adam Gorski, Albert Clapés, Andrei Boiarov, Anton Afanasiev, Artur Xarles, Atom Scott, ByoungKwon Lim, Calvin Yeung, Cristian Gonzalez, Dominic Rüfenacht, Enzo Pacilio, Fabian Deuser, Faisal Sami Altawijri, Francisco Cachón, HanKyul Kim, Haobo Wang, Hyeonmin Choe, Hyunwoo J Kim, Il-Min Kim, Jae-Mo Kang, Jamshid Tursunboev, Jian Yang, Jihwan Hong, Jimin Lee, Jing Zhang, Junseok Lee, Kexin Zhang, Konrad Habel, Licheng Jiao, Linyi Li, Marc Gutiérrez-Pérez, Marcelo Ortega, Menglong Li, Milosz Lopatto, Nikita Kasatkin, Nikolay Nemtsev, Norbert Oswald, Oleg Udin, Pavel Kononov, Pei Geng, Saad Ghazai Alotaibi, Sehyung Kim, Sergei Ulasen, Sergio Escalera, Shanshan Zhang, Shuyuan Yang, Sunghwan Moon, Thomas B. Moeslund, Vasyl Shandyba, Vladimir Golovkin, Wei Dai, WonTaek Chung, Xinyu Liu, Yongqiang Zhu, Youngseo Kim, Yuan Li, Yuting Yang, Yuxuan Xiao, Zehua Cheng, Zhihao Li,
- Abstract summary: SoccerNet 2024 challenges represent the fourth annual video understanding challenges organized by the SoccerNet team.
The challenges aim to advance research across multiple themes in football, including broadcast video understanding, field understanding, and player understanding.
This year, the challenges encompass four vision-based tasks.
- Score: 152.8534707514927
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The SoccerNet 2024 challenges represent the fourth annual video understanding challenges organized by the SoccerNet team. These challenges aim to advance research across multiple themes in football, including broadcast video understanding, field understanding, and player understanding. This year, the challenges encompass four vision-based tasks. (1) Ball Action Spotting, focusing on precisely localizing when and which soccer actions related to the ball occur, (2) Dense Video Captioning, focusing on describing the broadcast with natural language and anchored timestamps, (3) Multi-View Foul Recognition, a novel task focusing on analyzing multiple viewpoints of a potential foul incident to classify whether a foul occurred and assess its severity, (4) Game State Reconstruction, another novel task focusing on reconstructing the game state from broadcast videos onto a 2D top-view map of the field. Detailed information about the tasks, challenges, and leaderboards can be found at https://www.soccer-net.org, with baselines and development kits available at https://github.com/SoccerNet.
Related papers
- Deep Understanding of Soccer Match Videos [20.783415560412003]
Soccer is one of the most popular sport worldwide, with live broadcasts frequently available for major matches.
Our system can detect key objects such as soccer balls, players and referees.
It also tracks the movements of players and the ball, recognizes player numbers, classifies scenes, and identifies highlights such as goal kicks.
arXiv Detail & Related papers (2024-07-11T05:54:13Z) - SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap [102.5232204867158]
We formalize the task of Game State Reconstruction and introduce SoccerNet-GSR, a novel Game State Reconstruction dataset focusing on football videos.
SoccerNet-GSR is composed of 200 video sequences of 30 seconds, annotated with 9.37 million line points for pitch localization and camera calibration.
Our experiments show that GSR is a challenging novel task, which opens the field for future research.
arXiv Detail & Related papers (2024-04-17T12:53:45Z) - SoccerNet 2023 Challenges Results [165.5977813812761]
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.
arXiv Detail & Related papers (2023-09-12T07:03:30Z) - GOAL: A Challenging Knowledge-grounded Video Captioning Benchmark for
Real-time Soccer Commentary Generation [75.60413443783953]
We present GOAL, a benchmark of over 8.9k soccer video clips, 22k sentences, and 42k knowledge triples for proposing a challenging new task setting as Knowledge-grounded Video Captioning (KGVC)
Our data and code are available at https://github.com/THU-KEG/goal.
arXiv Detail & Related papers (2023-03-26T08:43:36Z) - SoccerNet 2022 Challenges Results [167.6158475931228]
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.
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.
arXiv Detail & Related papers (2022-10-05T16:12:50Z) - SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of
Broadcast Soccer Videos [71.72665910128975]
SoccerNet-v2 is a novel large-scale corpus of manual annotations for the SoccerNet video dataset.
We release around 300k annotations within SoccerNet's 500 untrimmed broadcast soccer videos.
We extend current tasks in the realm of soccer to include action spotting, camera shot segmentation with boundary detection.
arXiv Detail & Related papers (2020-11-26T16:10:16Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.