This project aims to analyze real-time football, detect and track players, referees, and ball in a video using YOLO, a state-of-the-art AI object detection model. The model's performance was enhanced through custom training. Players are assigned to teams based on their t-shirt colors using K-means for pixel segmentation and clustering. This allows to measure a team's ball acquisition percentage during a match.
Optical flow was employed to measure camera movement between frames, enabling precise measurement of player movement. Perspective transformation was implemented to represent the scene's depth and perspective, allowing us to measure player movement in meters rather than pixels.
Finally, player speed and the distance covered was calculated to provide in depth statistics.
The projects uses Poetry to manage dependencies. All the dependencies are in pyproject.toml. To install the them, run
the following command:
poetry installA screenshots from the output video:
- YOLO v8 for player, referee, and ball detection, YOLO performance was enhanced through custom training
- Kmeans for pixel segmentation and clustering to detect t-shirt color
- Optical Flow to measure camera movement
- Perspective Transformation to represent scene depth and perspective
- Speed and distance calculation per player
Training for the players, referees, and ball detection is written in notebook:
src/training/football_training_yolo.ipynb