  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.
