Welcome to the SoccerNet Development Kit for the Replay Grounding Task and Challenge. This kit is meant as a help to get started working with the soccernet data and the proposed task. More information about the dataset can be found on our official website.
SoccerNet Replay Grounding is part of the SoccerNet-v2 dataset, which is an extension of SoccerNet-v1 with new and challenging tasks including action spotting, camera shot segmentation with boundary detection, and a novel replay grounding task.
The Action Spotting dataset consists of 500 complete soccer games including:
- Full untrimmed broadcast videos in both low and high resolution.
- Pre-computed features such as ResNET-152.
- Annotations of camera changes and replays with live action spot (Labels-cameras.json).
Participate in our upcoming Challenge in the CVPR 2022 International Challenge on Activity Recognition Workshop and try to win up to 500$ sponsored by SportRadar! All details are available on the challenge website, or on the main page.
The participation deadline is fixed at the 30th of May 2022. The official rules and guidelines are available on ChallengeRules.md.
This table summarizes the current performances on the 2021 challenge. For the leaderboard on the 2022 challenge, please visit EvalAI test and challenge leaderboards.
| Model | tight Avg-AP (challenge) | Avg-AP (challenge) | tight Avg-AP (test) | Avg-AP (test) |
|---|---|---|---|---|
| Baidu Research | TBD | 71.90% | 25.55% | 76.00% |
| OPPO | TBD | 63.91% | NA | NA |
| Xinhuazhiyun | TBD | 41.08% | NA | NA |
| CALF_more_negatives | TBD | 40.75% | NA | NA |
| CALF | TBD | 31.28 | TBD | 32.39% |
| NetVLAD | TBD | 25.13% | TBD | 24.57% |
This table summarizes the current performances of published methods only. Last update January 2022.
| Model | tight Avg-AP (challenge) | Avg-AP (challenge) | tight Avg-AP (test) | Avg-AP (test) |
|---|---|---|---|---|
| CALF_more_negatives | TBD | 40.75% | NA | NA |
| CALF | TBD | 31.28 | TBD | 32.39% |
| NetVLAD | TBD | 25.13% | TBD | 24.57% |
A SoccerNet pip package to easily download the data and the annotations is available.
To install the pip package simply run:
pip install SoccerNet
Then use the API to downlaod the data of interest:
from SoccerNet.Downloader import SoccerNetDownloader
mySoccerNetDownloader = SoccerNetDownloader(LocalDirectory="/path/to/SoccerNet")
mySoccerNetDownloader.downloadGames(files=["Labels-cameras.json"], split=["train","valid","test"])
If you want to download the videos, you will need to fill a NDA to get the password.
mySoccerNetDownloader.password = input("Password for videos?:\n")
mySoccerNetDownloader.downloadGames(files=["1_224p.mkv", "2_224p.mkv"], split=["train","valid","test","challenge"])
mySoccerNetDownloader.downloadGames(files=["1_720p.mkv", "2_720p.mkv", "video.ini"], split=["train","valid","test","challenge"])
We provide several features including ResNET (used for our benchmarks), and last year's winners features from Baidu Research. Check out our pip package documentation for more features.
mySoccerNetDownloader.password = input("Password for videos?:\n")
mySoccerNetDownloader.downloadGames(files=["1_ResNET_TF2_PCA512.npy", "2_ResNET_TF2_PCA512.npy"], split=["train","valid","test","challenge"])
mySoccerNetDownloader.downloadGames(files=["1_baidu_soccer_embeddings.npy", "2_baidu_soccer_embeddings.npy", "video.ini"], split=["train","valid","test","challenge"])
As it was one of the most requested features on SoccerNet-V1, check out the action spotting repository to extract the features.
This repository contains several benchmarks for replay grounding, which are presented in the SoccerNet-V2 paper. You can use these codes to build upon our methods and improve the performances.
This repository and the pip package provide evaluation functions for the proposed tasks based on predictions saved in the JSON format. See the Evaluation folder of this repository for more details.
Finally, this repository provides the Annotation tool used to annotate the camera changes and replays. This tool can be used to visualize the information. Please follow the instruction in the dedicated folder for more details.
Check out our other challenges related to SoccerNet!
For further information check out the paper and supplementary material: https://openaccess.thecvf.com/content/CVPR2021W/CVSports/papers/Deliege_SoccerNet-v2_A_Dataset_and_Benchmarks_for_Holistic_Understanding_of_Broadcast_CVPRW_2021_paper.pdf
Please cite our work if you use our dataset:
@InProceedings{Deliège2020SoccerNetv2,
title={SoccerNet-v2 : A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos},
author={Adrien Deliège and Anthony Cioppa and Silvio Giancola and Meisam J. Seikavandi and Jacob V. Dueholm and Kamal Nasrollahi and Bernard Ghanem and Thomas B. Moeslund and Marc Van Droogenbroeck},
year={2021},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
}For further information about the challenge, check out the paper and supplementary material: https://arxiv.org/abs/2210.02365
Please cite our work if you use the SoccerNet dataset:
@inproceedings{Giancola_2022,
doi = {10.1145/3552437.3558545},
url = {https://doi.org/10.1145%2F3552437.3558545},
year = 2022,
month = {oct},
publisher = {{ACM}},
author = {Silvio Giancola and Anthony Cioppa and Adrien Deli{\`{e}}ge and Floriane Magera and Vladimir Somers and Le Kang and Xin Zhou and Olivier Barnich and Christophe De Vleeschouwer and Alexandre Alahi and Bernard Ghanem and Marc Van Droogenbroeck and Abdulrahman Darwish and Adrien Maglo and Albert Clap{\'{e}}s and Andreas Luyts and Andrei Boiarov and Artur Xarles and Astrid Orcesi and Avijit Shah and Baoyu Fan and Bharath Comandur and Chen Chen and Chen Zhang and Chen Zhao and Chengzhi Lin and Cheuk-Yiu Chan and Chun Chuen Hui and Dengjie Li and Fan Yang and Fan Liang and Fang Da and Feng Yan and Fufu Yu and Guanshuo Wang and H. Anthony Chan and He Zhu and Hongwei Kan and Jiaming Chu and Jianming Hu and Jianyang Gu and Jin Chen and Jo{\~{a}}o V. B. Soares and Jonas Theiner and Jorge De Corte and Jos{\'{e}} Henrique Brito and Jun Zhang and Junjie Li and Junwei Liang and Leqi Shen and Lin Ma and Lingchi Chen and Miguel Santos Marques and Mike Azatov and Nikita Kasatkin and Ning Wang and Qiong Jia and Quoc Cuong Pham and Ralph Ewerth and Ran Song and Rengang Li and Rikke Gade and Ruben Debien and Runze Zhang and Sangrok Lee and Sergio Escalera and Shan Jiang and Shigeyuki Odashima and Shimin Chen and Shoichi Masui and Shouhong Ding and Sin-wai Chan and Siyu Chen and Tallal El-Shabrawy and Tao He and Thomas B. Moeslund and Wan-Chi Siu and Wei Zhang and Wei Li and Xiangwei Wang and Xiao Tan and Xiaochuan Li and Xiaolin Wei and Xiaoqing Ye and Xing Liu and Xinying Wang and Yandong Guo and Yaqian Zhao and Yi Yu and Yingying Li and Yue He and Yujie Zhong and Zhenhua Guo and Zhiheng Li},
title = {{SoccerNet} 2022 Challenges Results},
booktitle = {Proceedings of the 5th International {ACM} Workshop on Multimedia Content Analysis in Sports}
}