Badminton trajectory tracking method based on binocular stereo vision and YOLOv8
Yuan Zhang · Discover Applied Sciences · 2026
Badminton trajectory tracking has important applications in sports training analysis and intelligent companion systems. However, current methods face several challenges. High-speed motion makes it difficult for traditional algorithms to capture continuous trajectories. The small target size leads to missed detections during long-distance tracking due to feature loss. Complex environments reduce tracking stability. This paper puts forward a badminton trajectory tracking model based on Binocular Stereo Vision and an improved You Only Look Once version 8 algorithm. The model uses the improved Retina Cortex Theory to enhance the image contrast, applies a matching algorithm to optimize binocular disparity for higher depth accuracy, and integrates a You Only Look Once version 8 algorithm with a Convolutional Block Attention Module for stronger target detection. Experimental results show that the attention-based You Only Look Once version 8 algorithm achieves an average precision of 96.22%, with 97.61% for small targets and 98.64% for medium targets, while the mean absolute error is only 1.26%. The proposed model achieved a tracking success rate of 98.44% and a multi-object tracking accuracy of 96.94% on badminton trajectory and action recognition datasets, with a processing frame rate of 60 and a trajectory breakage rate of only 1.38%. These results outperform traditional models significantly. This research provides technical support for high-precision badminton trajectory tracking and promotes the application of computer vision in intelligent sports systems.