MatchVision

Automated football match analysis using Computer Vision.

## About The Project GitHub: [click here](https://github.com/janskn/football-computer-vision) The project called *MatchVision* allows tracking the ball, referees and players, treating players individually by ID and assigning them to a team, displaying ball possession and estimating the camera movement.
**Training** The model was trained on Google Colab's servers with T4 GPU (~ 45 mins.) It uses a custom trained YOLOv5 model. Benefits: - better ball tracking - not tracking persons outside the pitch
**Structure** The YOLO model detects the bounding boxes of the objects. They get stored in a dictionary along with the tracking ID, the team ID, the position etc. The team detection is based on KMeans clustering. To improve the ball highlighting, the ball position gets interpolated with Pandas. The camera movement gets estimated with optical flow to adjust the object positions.
**Input Video Example**

**Result**

### Built With The source code was built with Python, mainly using Ultralytics, OpenCV and NumPy. ## Usage You have 2 options to run the project:
**1) Command Line** **2) Frontend** You can upload your own video or use a demo video. Everything is explained in the frontend.