A Computer Vision Approach to Analyzing Player Movement

Abstract

This thesis provides a new resource for squash performance analysis by developing a computer vision system that integrates advanced object detection and tracking techniques. By stringing together YOLOv8 for precise player detection and StrongSORT for multi-object tracking, the system accurately processes game footage to collect player data. A tool developed in this project is a user-assisted manual court mapping interface corrects perspective distortions, providing the resource to generate movement based analytics that reflect on-court dynamics, such as control of the critical ’T’ position. The adaptability of the technologies used create the opportunity for the expansion of this project. Further development of this project offers valuable insight for coaching and performance improvement, and further refinements are expected to enhance the accuracy of detection and tracking even further.