MATLAB Implementation of Motion Estimation Using Block Matching Algorithm

This article presents a MATLAB implementation method for motion estimation using the block matching algorithm. Block matching is a widely used technique in computer vision and image processing applications, primarily employed for measuring motion between two consecutive image frames. The implementation process involves several key stages: image preprocessing, feature extraction, and matching algorithms. Initially, image preprocessing is performed to enhance matching accuracy through operations such as noise reduction and contrast adjustment using MATLAB functions like imfilter and histeq. Subsequently, feature extraction algorithms identify key points or regions of interest within the images, utilizing techniques such as corner detection (Harris corner detector) or intensity-based feature identification. Finally, the core block matching algorithm is implemented, where images are divided into blocks and corresponding blocks between frames are matched using similarity measures like Sum of Absolute Differences (SAD) or Mean Squared Error (MSE) computed through functions such as normxcorr2 for normalized cross-correlation. This process generates motion vectors that represent the displacement between corresponding blocks. Through this comprehensive guide, you will learn practical MATLAB implementation techniques for block-based motion estimation and understand its broad applications in computer vision tasks such as video compression, object tracking, and optical flow analysis.