Source Code for Moving Object Tracking Using Mean-Shift Method

This article presents a comprehensive implementation of moving object tracking using the mean-shift method along with complete source code. We discuss the algorithm's core components including kernel density estimation, histogram computation for target modeling, and iterative mean-shift vector calculation for target localization. The implementation addresses critical aspects such as color feature extraction using RGB/HSV color spaces, bandwidth selection for kernel functions, and convergence criteria for tracking robustness. We analyze both the advantages (computational efficiency, real-time performance) and limitations (scale adaptation challenges, occlusion handling) of the approach, supported by practical application scenarios in surveillance and video analysis. The code includes key functions for target initialization, histogram back-projection, and mean-shift iteration with optimization techniques. Additional resources and references are provided to help researchers deepen their understanding and extend the implementation for specific tracking requirements. This work serves as a valuable reference for developers and researchers working on computer vision and motion tracking applications.