Classic Lucas-Kanade Algorithm Implementation

This article explores the classic implementation of the Lucas-Kanade algorithm, one of the most renowned optical flow estimation methods in computer vision. We examine the algorithm's underlying principles, including its gradient-based approach that assumes brightness constancy and small motion between consecutive frames. The implementation utilizes a least squares solution over local pixel neighborhoods, where the code typically involves computing spatial and temporal gradients, constructing the structure tensor matrix, and solving the optical flow equations. Key functions generally include image pyramid construction for handling larger displacements and iterative refinement steps for improved accuracy. While the algorithm excels in computational efficiency and sub-pixel precision, it faces limitations with large motions and textureless regions. We provide complete source code demonstrating practical implementation aspects like window size selection, pyramid levels, and convergence criteria, along with relevant research papers to facilitate deeper understanding of the algorithm's applications in computer vision research and practice.