Analysis of Volleyball Athlete Training Trajectory Data Capture Based on Mean Shift Algorithm
Junjie Liu
International Journal of Web Applications, Vol. 17, No. 2, 2025.
DOI: https://doi.org/10.6025/ijwa/2025/17/2/70-77
Link: https://www.dline.info/ijwa/fulltext/v17n2/ijwav17n2_3.pdf
Abstract:
With the improvement of sports competition level, the requirements for sports training are constantly increasing. To overcome the difficulties of trajectory acquisition brought about by complex backgrounds and fast target motion, this paper proposes a sports training trajectory data capture technique based on the mean shift algorithm. The human body model is regarded as a skeletal model with 51 degrees of freedom and 16 joints, enabling the digitization of training trajectories. Dimension reduction is applied to the trajectory data to reduce computational load. To reduce the dependence of the mean shift algorithm on environmental parameters, the probability density function in the gradient iteration estimation algorithm is selected, utilizing the color information of the target as a feature to capture trajectory data. Experiments demonstrate that the method can accurately capture the movement of each joint in the athlete and complete the capture of training trajectory data without relying on related parameters.