players_football Computer Vision Project
Updated 2 months ago
Metrics
Description
Here are a few use cases for this project:
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Sports Analysis and Strategy: The "players_football" model could be used to analyse football games in real-time or retrospectively, charting the movements of both the players and the ball. Coaches can use this data to review team strategies, individual player performance, and opposition tactics.
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Game Highlights Creation: The model can be employed by sports media outlets to automatically produce game highlights by identifying key moments like close interactions between the player and the ball.
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Injury Prevention and Player Safety: By monitoring player and ball movements, potential collisions can be predicted and avoided, potentially reducing the risk of injuries.
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Virtual and Augmented Reality Gaming: In the realm of AR/VR, the model can be applied to develop immersive, realistic football gaming experiences by using real-life player and ball data.
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Interactive Audience Experience: On a broadcasting level, the "players_football" model can enhance audience engagement by providing detailed analytics, player tracking information, and interesting sport insights to viewers in real-time.
Use This Trained Model
Try it in your browser, or deploy 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{
players_football_dataset,
title = { players_football Dataset },
type = { Open Source Dataset },
author = { cp },
howpublished = { \url{ https://universe.roboflow.com/cp-vsyki/players_football } },
url = { https://universe.roboflow.com/cp-vsyki/players_football },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2025 },
month = { mar },
note = { visited on 2025-05-16 },
}