Digitization and Analysis of Sports Training Trajectories Using Dimensionality Reduction
Bing Xie
Journal of Networking Technology, Vol. 16, No. 4, 2025.
DOI: https://doi.org/10.6025/jnt/2025/16/4/155-162
Link: https://www.dline.info/jnt/fulltext/v16n4/jntv16n4_2.pdf
Abstract:
This paper presents an innovative method for digitizing and analyzing sports training trajectory data using the mean shift algorithm combined with color histogram features for robust motion tracking. The authors model the human body as a 16 joint skeletal framework, representing joint rotations via quaternions mapped in to  space to avoid singularities associated with Euler angles. To manage high dimensional motion capture data, they apply a linear time invariant system for dimensionality reduction. The mean shift algorithm chosen for its non parametric nature and resilience to object deformation, rotation, and speed variations is used to track athletesâ€™ motion paths by leveraging consistent color features across video frames. Experimental results demonstrate that this approach outperforms both Space Mocap and MATLAB based trajectory methods in accuracy and error rate. The study also integrates Dynamic Time Warping (DTW) to compare motion sequences, showing that intra class motion distances are significantly smaller than inter class ones, thereby enabling effective behavior classification. Motion sequences are categorized into dynamic (e.g., jumps, squats) and static (e.g., lifting, waving) actions. The system achieves precise segmentation and recognition of human activities, even under conditions of occlusion or changes in posture. Supported by motion capture databases and validated through comparative analysis, the proposed technique offers a reliable, parameterfree solution for real time athlete tracking and motion behavior analysis in sports science applications.