MATLAB Multi-Object Tracking Code with Background Subtraction

This MATLAB code package contains a multi-object tracker implementation with accompanying documentation. The system utilizes background subtraction methodology that supports real-time background model updates through adaptive thresholding and frame differencing techniques. While the tracker demonstrates robust performance when detecting and tracking a limited number of targets, its effectiveness substantially decreases when tracking more than 8 objects simultaneously due to computational constraints and occlusion handling limitations. To address these limitations, several enhancement approaches can be implemented: - Deploy multiple camera systems to distribute tracking workload across different surveillance zones, reducing individual tracker burden through spatial partitioning. - Enhance the tracking algorithm by integrating complementary methods alongside background subtraction, such as Kalman filtering for motion prediction or optical flow for trajectory analysis, to improve accuracy and stability. - Implement advanced feature analysis techniques including histogram of oriented gradients (HOG) or deep learning-based feature extractors to enable more precise object classification and persistent tracking. By incorporating these methodological improvements, the tracking system's capacity can be significantly enhanced to handle more complex multi-object tracking scenarios with improved robustness and scalability.