Classic Motion Estimation Algorithms: MATLAB Implementation and Analysis

In this article, we will explore classic motion estimation algorithms and their applications in computer vision. Modern computer vision systems rely heavily on precise motion estimation algorithms to infer object movement in images and videos. Among the fundamental techniques are the Lucas-Kanade optical flow method and block matching approaches. The Lucas-Kanade optical flow method is a widely-used technique for estimating pixel-level motion in images, operating under the assumption that local pixel movements can be approximated as global motion within small regions. Our MATLAB implementation will demonstrate how to compute optical flow vectors using gradient-based methods and solve the system of equations through least squares optimization. Key functions like vision.OpticalFlow and opticalFlowLK will be discussed along with practical parameter tuning considerations. Block matching is another essential visual measurement technique based on search algorithms that identifies matching feature points across image sequences to compute motion vectors. Our MATLAB code will showcase how to implement exhaustive search and diamond search patterns using functions such as vision.BlockMatcher, while explaining critical parameters like block size, search range, and matching criteria (MAD, MSE, or SAD). The implementation will include optimization techniques to reduce computational complexity while maintaining accuracy. Furthermore, we will develop comprehensive MATLAB programs to implement these algorithms and apply them to real movie sequences. The code examples will illustrate practical considerations for handling different video formats, managing computational efficiency, and visualizing motion vector fields. These implementations will demonstrate how to apply these classic algorithms in real-world scenarios for robust motion estimation, including error analysis and performance evaluation metrics for algorithm comparison.