NewVersionAthleticdetection Computer Vision Project
Updated a year ago
Here are a few use cases for this project:
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Sports Coaching: The model can be used to analyze athletes' performance during the race. Factors such as jumping pattern over hurdles, running efficiency, and stride frequency can be analyzed for coaching and performance improvements.
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Broadcast Enhancement: During sports events broadcast, the model can provide real-time identification of athletes, helping commentators and viewers to keep track of the competition's progression.
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Surveillance and Security: During track and field events, the system can be employed for crowd control and security by identifying unexpected hurdles or unapproved athletes on the field.
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Event Organization: Event planners can use the model to study the flow of athletes and hurdles on the track to design more efficient arrangements or layouts for future events.
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Injury Prevention: The model can help study athlete movements with reference to hurdle clearing to assist in reducing accident and injury risk in races. By studying movements, trainers could work with athletes to refine their techniques.
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Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
newversionathleticdetection-pneir_dataset,
title = { NewVersionAthleticdetection Dataset },
type = { Open Source Dataset },
author = { Aela Le Sommer },
howpublished = { \url{ https://universe.roboflow.com/aela-le-sommer-e9m2y/newversionathleticdetection-pneir } },
url = { https://universe.roboflow.com/aela-le-sommer-e9m2y/newversionathleticdetection-pneir },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { aug },
note = { visited on 2024-11-22 },
}