Classic Lucas-Kanade Optical Flow Algorithm Implementation in MATLAB

In the field of computer vision, optical flow represents a fundamental concept describing the motion of pixels across image sequences over time. The Lucas-Kanade method stands as a classical optical flow estimation algorithm that employs a local window approach to compute motion vectors for individual pixels. This algorithm proves particularly valuable for video object tracking applications, as it enables precise determination of moving object positions and facilitates trajectory prediction. Implementing the Lucas-Kanade algorithm in MATLAB involves several key computational steps: first, calculating spatial and temporal gradients using built-in functions like gradient() or imgradient(); second, constructing the structure tensor matrix for each local window; third, solving the linear system using matrix inversion techniques with pinv() for stability; and finally, validating results with thresholding and consistency checks. The algorithm's efficiency stems from its assumption of constant flow within small neighborhoods, making it computationally tractable for real-time applications. MATLAB's matrix operations and image processing toolbox significantly simplify the implementation process, contributing to its widespread adoption in both research and industrial applications.