Tennis Computer Vision Project
Updated 2 years ago
Metrics
Here are a few use cases for this project:
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Sports Analytics: This "Tennis" model can be instrumental for sports analysts to break down the gameplay of tennis players, track ball possession, speed, trajectory, and player's positions, enhancing their ability to interpret game strategies and performance.
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Event Organizing: Event organizers for tennis tournaments can use this model to monitor crowd size, identify peak engagement moments, or ensure player safety by accurately spotting tennis players and tracking the exact location of the ball.
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Training and Coaching: Tennis coaches can leverage this model to analyze player's techniques, ball movements, strike positions, and footwork from recorded training sessions. This can aid in providing personalized feedback and training programs.
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Broadcast Enhancement: Media companies can make use of the "Tennis" model in their broadcasts to provide real-time analytics, player tracking, ball speed, and trajectory to viewers, enriching the viewing experience.
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Player Performance Simulation: Game developers could use this model for creating more realistic tennis computer games or virtual reality experiences, where the movements of professional players and ball dynamics are translated into the game algorithm.
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{
tennis-yfcgx_dataset,
title = { Tennis Dataset },
type = { Open Source Dataset },
author = { Deep },
howpublished = { \url{ https://universe.roboflow.com/deep-hbapi/tennis-yfcgx } },
url = { https://universe.roboflow.com/deep-hbapi/tennis-yfcgx },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { jan },
note = { visited on 2025-04-25 },
}