Object Detection

Soccer-Game Computer Vision Project

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Here are a few use cases for this project:

  1. Soccer Match Analysis: This model can be used to track individual players' movements and possessions throughout a soccer match in real time, providing valuable information to coaches, statisticians, and soccer analysts.

  2. Player Performance Tracking: The Soccer-Game vision model can be used to quantify individual players' contributions by identifying specific actions, such as number of touches on the ball, distance ran, and contributions to offensive and defensive plays.

  3. Automated Sports Broadcasting: The model can be used to automatically recognize the players on the field. This will make it more convenient for auto-cursoring certain visual highlights during live broadcasts.

  4. Sciences Sports Training: By tracking each soccer player actions during a game, the model can provide insights about players' strengths and weaknesses. This analysis can then be incorporated into training drills and strategies.

  5. Gaming and Virtual Reality: The model can be used in developing more complex football video games or Virtual Reality experiences, where the actions of the virtual players are based on the behaviors of real-life soccer players.

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

Cite this Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{ soccer-game_dataset,
    title = { Soccer-Game Dataset },
    type = { Open Source Dataset },
    author = { MODU Labs },
    howpublished = { \url{ } },
    url = { },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2022 },
    month = { aug },
    note = { visited on 2023-12-05 },

Find utilities and guides to help you start using the Soccer-Game project in your project.



Last Updated

a year ago

Project Type

Object Detection




(FR)Player, (FR-DF)02-PierrKalulu, (FR-DF)13-ClemairMisulang, (FR-DF)15-ModiboSanang, (FR-DF)17-AngtonoKassi, (FR-FW)07-ArnoNordang, (FR-FW)09-Natanel Umbuk, (FR-FW)10-AngdreiPieree, (FR-FW)18-KoloMuani, (FR-GK)01-Paul Bernardoni, (FR-MF)06-RukaTosar, (FR-MF)08-EnjoRepee, (FR-MF)11-TejiSabanie, (FR-MF)12-Alexi Beca, (FR-MF)14-FloriangTobanng, (FR-OF)03-Elvin Barr, (FR-OF)19-Ismael Dukure, (KR)Player, (KR-DF)02-JeongTaeWook, (KR-DF)06-KangWoonSeong, (KR-DF)15-LeeYooHyun, (KR-DF)21-LeeSangMin, (KR-FW)09-KweonChangHoon, (KR-FW)10-LeeDongJun, (KR-FW)12-SongMinKyu, (KR-FW)16-HwangYiJo, (KR-FW)19-EumWeonSang, (KR-GK)22-SongBeumGun, (KR-MF)05-KimJinKyu, (KR-MF)08-KimDongHyun, (KR-MF)11-LeeDongKyeong, (KR-MF)14-LeeGangIn, (KR-MF)17-WonDuJae, (KR-MF)20-JeongSeungWon, (KR-OF)07-KimJinYa, b, person, referee, sports ball, v, yolov5

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