MATLAB Implementation of Optical Flow Algorithm for Single and Multiple Moving Object Detection

This document discusses the implementation of optical flow algorithm code designed to detect both single moving objects and multiple moving targets. Optical flow represents a fundamental technique in computer vision that derives object motion by analyzing changes in pixel values across consecutive image frames. The algorithm typically involves calculating velocity vectors for each pixel, often implemented using gradient-based methods like Horn-Schunck or Lucas-Kanade approaches. Code implementation usually includes key functions for frame differencing, gradient computation, and iterative optimization to solve the optical flow constraint equation. This technology finds extensive applications across various domains including robotics, autonomous vehicles, facial tracking systems, and motion analysis. In this implementation, we explore core algorithmic principles such as brightness constancy assumptions and spatial coherence, along with practical applications in target detection and tracking scenarios where the code processes video sequences to extract motion vectors and segment moving objects from background scenes.