Horn-Schunck Optical Flow Method
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The Horn-Schunck optical flow method is a classical algorithm for tracking object motion in video sequences. This technique analyzes pixel intensity changes between consecutive frames and estimates motion vectors based on global smoothness constraints. The method formulates optical flow estimation as an optimization problem minimizing both the brightness constancy term and the spatial coherence term. Widely applied in computer vision and video processing, it enables key applications like object tracking and motion detection through iterative refinement using Euler-Lagrange equations. The core implementation typically involves solving partial differential equations with Gaussian-Seidel relaxation methods, where the optical flow vectors (u,v) are updated iteratively using neighborhood averages while preserving flow field smoothness.
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