Papers › Efficient Golf Ball Detection and Tracking Based on Convolutional Neural Networks and...
Efficient Golf Ball Detection and Tracking Based on Convolutional Neural Networks and Kalman Filter
Tianxiao Zhang, Xiaohan Zhang, Yiju Yang, Zongbo Wang, Guanghui Wang
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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections