Mean Shift Object Tracking Algorithm Implementation

This project presents a MATLAB implementation of the mean shift tracking algorithm, a fundamental non-parametric technique widely employed in computer vision for robust object tracking in video sequences. The core algorithm operates through an iterative density estimation process where a kernel-based window progressively shifts toward regions of highest data point concentration. Key implementation aspects include histogram-based target modeling using color distributions and Bhattacharyya coefficient calculation for similarity measurement between target and candidate regions. The MATLAB implementation leverages built-in functions for efficient matrix operations and visualization, incorporating features such as adaptive kernel bandwidth selection and convergence criteria monitoring. Through iterative mean shift vector computation, the tracker effectively follows target objects while maintaining robustness against partial occlusions and illumination variations. The code structure facilitates easy parameter tuning for different tracking scenarios, with clear separation between initialization, kernel density estimation, and position update modules. This implementation demonstrates practical applications of mean shift theory, providing researchers with a flexible framework for analyzing tracking performance, testing different kernel functions, and extending the algorithm for multi-object tracking scenarios. The modular design allows for straightforward integration of additional features like scale adaptation and background suppression, making it suitable for both educational purposes and advanced computer vision research.