Dodgeball Computer Vision Project
Updated 2 years ago
Metrics
Here are a few use cases for this project:
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Sports Analytics: The model can be used to track player and ball movement in real-time for dodgeball games, providing useful data to coaches for improving team strategies. This can include analyzing player reaction times, ball throwing accuracy, and defensive tactics.
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Player Training Aid: The "Dodgeball" model can be integrated into a training system to provide detailed feedback to players on how they are throwing and dodging balls, helping them improve their skills.
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Event Monitoring: The model can be used by event organizers to monitor dodgeball games or training sessions to ensure safety protocols are being followed, such as the appropriate distancing between players and observer interfering prevention.
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Interactive Gaming: The computer vision model can be applied to create an interactive gaming experience where a user's movements are tracked for throwing and dodging virtual balls in a fitness or entertainment game.
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Automated Video Editing: For event cinematographers or vloggers, the model can automate the process of identifying key moments in dodgeball games - like a game-winning throw or an impressive dodge - and compile these highlights into a single video.
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Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
dodgeball-f7dp5_dataset,
title = { Dodgeball Dataset },
type = { Open Source Dataset },
author = { Kandidat },
howpublished = { \url{ https://universe.roboflow.com/kandidat-pgnqx/dodgeball-f7dp5 } },
url = { https://universe.roboflow.com/kandidat-pgnqx/dodgeball-f7dp5 },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { apr },
note = { visited on 2024-11-15 },
}