Robust Color Feature Point Tracking for Moving Objects

This paper presents a robust MATLAB implementation designed for advanced motion analysis. The program employs sophisticated color-based feature detection algorithms to track distinctive points on moving objects. Through frame-by-frame image processing, it extracts spatial-temporal data and performs visual computations using techniques such as blob analysis and motion vector calculation. Key functions include color space conversion (RGB to HSV for better color isolation) and feature correlation across consecutive frames using normalized cross-correlation methods. The system outputs comprehensive state information including velocity profiles (calculated via displacement/time differentials) and precise positional coordinates (derived from pixel-to-world coordinate transformations). Validated through rigorous multi-environment testing, this implementation demonstrates exceptional processing speed through optimized matrix operations and practical reliability, establishing itself as an essential tool for dynamic object localization in experimental setups. The code architecture modularizes core functionalities including background subtraction, feature point identification, and Kalman filtering for trajectory prediction.