2023/06/26 by Xinyu Wang, Jianwei Li, Wang, Xinyu +1
Computer Science · Economics, Econometrics and Finance · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Sports Analytics and Performance #Video Analysis and Summarization
paper · pdf · doi:10.48550/arxiv.2306.14492
openalex publication_date 2023/06/26 · openalex created_date 2023/06/28 · openalex updated_date 2026/07/28
Ball recognition and tracking have traditionally been the main focus of computer vision researchers as a crucial component of sports video analysis. The difficulties, such as the small ball size, blurry appearance, quick movements, and so on, prevent many classic methods from performing well on ball detection and tracking. In this paper, we present a method for detecting and tracking badminton balls. According to the characteristics of different ball speeds, two trajectory clip trackers are designed based on different rules to capture the correct trajectory of the ball. Meanwhile, combining contextual information, two rounds of detection from coarse-grained to fine-grained are used to solve the challenges encountered in badminton detection. The experimental results show that the precision, recall, and F1-measure of our method, reach 100%, 72.6% and 84.1% with the data without occlusion, respectively.