Real-time Golf Ball Detection and Tracking Based on Convolutional Neural Networks
Xiaohan Zhang, Tianxiao Zhang, Yi‐Ju Yang, Zongbo Wang, Guanghui Wang
Abstract
This paper focuses on the problem of real-time detection and tracking of a golf ball from video sequences. We propose an efficient and effective solution by integrating object detection and a discrete Kalman model. For ball detection, three classical convolutional neural network based detection models are implemented, including Faster R-CNN, YOLOv3, and YOLOv3 tiny. At the tracking stage, a discrete Kalman filter is employed to predict the location of the golf ball based on the previous observations. To increase the detection accuracy and speed, we propose to use image patches rather than the entire images for detection. In order to train the detection models and test the tracking algorithm, we collect and annotate a collection of golf ball dataset. Extensive experimental results are performed to demonstrate the effectiveness and superior performance of the proposed approach.