Mean-Shift Moving Object Tracking Source Code

In this article, the author presents an excellent mean-shift based moving object tracking source code and welcomes community support. The key strength of this implementation lies in its ability to track moving objects by calculating similarity between color histograms and target templates, making it widely applicable in image processing applications. The algorithm operates by iteratively shifting a kernel-weighted histogram toward the direction of maximum density increase in the feature space. The code efficiently implements Bhattacharyya coefficient calculation for similarity measurement between target and candidate regions. Additionally, the source code demonstrates excellent extensibility, allowing customization for specific requirements through parameter adjustments and kernel function modifications. Key functions include histogram back-projection, mean-shift vector computation, and convergence detection mechanisms. For those interested in image processing or computer vision fields, we highly recommend exploring this implementation as it provides valuable insights into practical motion tracking techniques and optimization strategies.