Tennis vision: computer vision for match analysis
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I built this project to explore how computer vision could be used to analyze tennis matches. The goal was simple: can we automatically track players and the ball, extract useful data about movement and performance, and provide insights that would be valuable for coaches and players?
The system uses computer vision techniques to detect and track players on the court, monitor ball movement throughout the match, and extract key points from the court layout. From this data, it calculates player and ball speeds, providing quantitative metrics that can help analyze performance.
What it does
Player detection and tracking: The system identifies and tracks players throughout the match, understanding their positioning and movement patterns.
Ball tracking: Sophisticated algorithms monitor the ball's movement, tracking its path from serve to point completion.
Court keypoint extraction: Critical points on the court are extracted to understand player positioning relative to the court layout.
Performance metrics: From the tracking data, the system calculates player and ball speeds, offering quantitative insights into match dynamics.
The challenge
Tennis is a fast sport. The ball moves quickly, players move dynamically, and the camera angle might not always be optimal. Building a system that can reliably track all these elements in real-time or from video footage requires handling:
- Fast-moving objects (the ball)
- Multiple subjects (both players)
- Occlusion (when players or the ball are hidden from view)
- Lighting variations
- Camera angle and perspective
This was a great learning project for diving into computer vision techniques, understanding how to handle real-world video analysis challenges, and exploring what's possible when you combine sports with technology.
The project is open source, so you can check out the code, experiment with the techniques, and see how computer vision can be applied to sports analysis.
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