Research on real – time tracking of table tennis ball based on machine learning with low-speed camera

摘要

This paper proposes a novel method to track table tennis ball in real time by a low-speed camera instead of a high-speed one. Several difficult problems are solved for practical applications, such as environmental interference, smear in low-speed video images and slow processing speed. In view of these difficulties, the VOCUS system is used to segment images and mark the three significant regions based on three contrast colour channels. These regions are utilized for image matching using the LGP+adaboost algorithm. As a strong classifier based on machine learning, adaboost algorithm can recognize the features of smear balls with different shapes. Therefore, the region that is most similar to smear ball from the three significant regions is regarded as a target. Afterwards, through the moving ROI area algorithm, the identification time is greatly shortened in real-time video tracking. Finally, the feasibility of the algorithm is examined by experiments.