Motion Estimation Using Block Matching Algorithm with MATLAB Implementation

This MATLAB implementation demonstrates motion estimation through the block matching method, returning a comprehensive motion vector matrix. Motion estimation serves as a critical task in computer vision, with extensive applications across various domains. The block matching approach represents a widely adopted technique for determining object movement between two temporal frames. The implementation involves partitioning images into multiple regions (typically 16x16 or 8x8 pixel blocks) and identifying the most corresponding pixel blocks between consecutive frames using similarity metrics like Mean Absolute Difference (MAD) or Sum of Absolute Differences (SAD). Key MATLAB functions include blockproc for image partitioning and custom search algorithms (full search or three-step search) for optimal block matching. While computationally intensive due to exhaustive search operations, this method provides accurate motion trajectory estimation. The code returns a motion matrix where each element contains displacement vectors (dx, dy) representing block movements between frames, enabling detailed motion analysis for your computer vision applications.