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I will build football player tracking and team classification using computer vision
AI sport football player tracking, ball tracking, data extraction
I'm AI and Computer Vision Developer specializing in football video analytics, player tracking, object detection, and sports data extraction. I build custom computer vision systems that turn football ...
À propos de ce service
Need to automatically identify, track and separate football players by team? I will build a custom computer vision system for football footage using YOLO, Python, OpenCV and tracking algorithms.
Your system can include:
- Player detection
- Persistent player IDs
- Multi-player tracking
- Team classification
- Jersey/color-based classification
- Player movement paths
- Tracking coordinates
- Annotated videos
- CSV/JSON data
- Custom football statistics
This is useful for football clubs, academies, coaches, analysts, researchers and sports-tech developers who need automated player tracking from match or training footage.
I can work from your existing model/codebase or develop the required computer vision pipeline around your project.
Tracking accuracy depends on factors such as camera angle, video resolution, lighting, player overlap and occlusion.
Please send a short sample of your footage and describe your desired output before ordering. I will evaluate the footage and recommend the appropriate approach for your project.
FAQ
How do you classify the teams?
Team classification can use jersey colors and other visual characteristics, depending on the footage
Can every player have a unique ID?
Yes. I can implement persistent player IDs, although heavy occlusion or camera changes can affect ID consistency.
Can you track substitutes or referees?
Yes, if they are visible and the detection/classification requirements are defined in advance.
Can you use my existing detection model?
Yes. I can integrate your existing YOLO or computer vision model into the tracking pipeline.
What video should I provide?
A short sample of the actual footage you want analyzed is ideal. This allows me to evaluate resolution, camera angle, lighting and player visibility before development.
