Efficient Golf Ball Detection and Tracking Based on Convolutional Neural Networks and Kalman Filter
This work provides an incremental improvement for golf ball detection and tracking, which could benefit sports analytics and broadcasting.
This paper addresses online golf ball detection and tracking in image sequences by combining CNN-based object detection with a Kalman filter. The method processes small image patches to improve small ball detection and uses the Kalman filter for prediction, achieving superior tracking performance.
This paper focuses on the problem of online golf ball detection and tracking from image sequences. An efficient real-time approach is proposed by exploiting convolutional neural networks (CNN) based object detection and a Kalman filter based prediction. Five classical deep learning-based object detection networks are implemented and evaluated for ball detection, including YOLO v3 and its tiny version, YOLO v4, Faster R-CNN, SSD, and RefineDet. The detection is performed on small image patches instead of the entire image to increase the performance of small ball detection. At the tracking stage, a discrete Kalman filter is employed to predict the location of the ball and a small image patch is cropped based on the prediction. Then, the object detector is utilized to refine the location of the ball and update the parameters of Kalman filter. In order to train the detection models and test the tracking algorithm, a collection of golf ball dataset is created and annotated. Extensive comparative experiments are performed to demonstrate the effectiveness and superior tracking performance of the proposed scheme.