A Badminton Recognition and Tracking System Based on Context
  Multi-feature Fusion
Xinyu Wang Jianwei Li
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.