[2604.08722] AI Driven Soccer Analysis Using Computer Vision

Computer Science > Computer Vision and Pattern Recognition arXiv:2604.08722 (cs) [Submitted on 9 Apr 2026] Title:AI Driven Soccer Analysis Using Computer Vision Authors:Adrian Manchado, Tanner Cellio, Jonathan Keane, Yiyang Wang View a PDF of the paper titled AI Driven Soccer Analysis Using Computer Vision, by Adrian Manchado and 3 other authors View PDF HTML (experimental) Abstract:Sport analysis is crucial for team performance since it provides actionable data that can inform coaching decisions, improve player performance, and enhance team strategies. To analyze more complex features from game footage, a computer vision model can be used to identify and track key entities from the field. We propose the use of an object detection and tracking system to predict player positioning throughout the game. To translate this to positioning in relation to the field dimensions, we use a point prediction model to identify key points on the field and combine these with known field dimensions to extract actual distances. For the player-identification model, object detection models like YOLO and Faster R-CNN are evaluated on the accuracy of our custom video footage using multiple different evaluation metrics. The goal is to identify the best model for object identification to obtain the most accurate results when paired with SAM2 (Segment Anything Model 2) for segmentation and tracking. For the key point detection model, we use a CNN model to find consistent locations in the soccer fie...