A Badminton Recognition and Tracking System Based on Context Multi-feature Fusion

TL;DR

This paper introduces a novel badminton ball detection and tracking system that combines multi-feature fusion and contextual information, achieving high accuracy in challenging conditions like small size and quick movements.

Contribution

It proposes a dual-trajectory clip tracker based on ball speed and a two-stage detection method leveraging contextual cues, improving badminton ball tracking performance.

Findings

Precision reaches 100% in non-occlusion scenarios

Recall is 72.6%, F1-measure 84.1% in experiments

Effective in handling small, fast-moving badminton balls

Abstract

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.