Rugby Ball Detection, Tracking and Future Trajectory Prediction Algorithm

This paper presents a custom object detection and tracking algorithm for position estimation and trajectory prediction of a moving rugby ball. The approach of the algorithm is to combine the accuracy of object detection provided by the custom trained YOLOv5 model and the speed of the KCF tracker to perform a linear trajectory prediction of the ball. Kalman filter is used to ensure the optimal estimation of the current position and increase the accuracy of prediction for the future trajectory. Multi-threading is implemented to concurrently detect and track the ball in consecutive frames, resulting a computationally efficient approach.