MeanShift Algorithm MATLAB Implementation with Detailed Code Documentation

This documentation presents a comprehensive MATLAB implementation of the MeanShift algorithm accompanied by a test image collection. The code features detailed inline comments that systematically explain the algorithmic workflow, starting with proper initialization of target models and candidate regions. The implementation clearly demonstrates weight calculation using kernel density estimation, Bhattacharyya distance computation for similarity measurement between distributions, and iterative target position updating through mean shift vector calculation. The provided test images allow users to validate the tracking performance across different scenarios. This resource serves as an ideal learning tool, offering practical insights into computer vision tracking algorithms with ready-to-execute MATLAB code that follows standard MeanShift procedural steps including histogram computation, gradient ascent optimization, and convergence checking.