One of our clients wanted to build an AI tool to analyse soccer matches and automatically derive insights that can help players identify the areas in which they need to improve. The goal was to build an AI tool capable of extracting detailed analytics from soccer match videos, including player identification and tracking, field detection, and player statistics including the number of shots they played, the duration for which they had the ball with them and the number of successful passes and intercepts they were able to make.
We engineered a robust multi-stage Computer Vision approach to handle the complexities of video analysis in a dynamic environment:
For detecting players and the ball, we trained an object detection model based on YOLO architecture. For training, we annotated dozens of videos of real soccer matches played at various academies, ensuring the model could recognize targets across varying light and field conditions.
We used the DeepSort algorithm to reliably track players and the ball throughout the duration of the match, collecting continuous trajectory data for analysis.
Since the camera often zooms in or players temporarily exit the frame, we needed a way of re-identifying a player when they re-appear. Because the videos we process are low quality (making facial recognition unusable), we developed a custom clustering technique based on different features, including jersey color and movement features, to correctly re-associate the player ID.
We use a separate ML model to detect which team a player belongs to by analyzing the color of the player's shirt, allowing for team-based statistics and tactical insights.
Once we know the location of the ball and which player has the ball at any time, we can easily detect key events such as passes, intercepts, and dribbles. For example, a pass is defined as the ball moving from one player to another player of the same team.
Our soccer analytics tool achieved:
This Custom Computer Vision solution provides a valuable competitive edge for sports academies seeking data-driven player improvement.
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