Web Reference: Figure 48.10 compares the optical flow computed using the gradient-based algorithm (i.e., one iteration) and the multiscale iterative refinement approach. Note how the gradient-based approach underestimates the motion of the left car. Instead of seeking to model optical flow directly, one can train a machine learning system to estimate optical flow. Since 2015, when FlowNet [15] was proposed, learning based models have been applied to optical flow and have gained prominence. Lucas-Kanade method computes optical flow for a sparse feature set (in our example, corners detected using Shi-Tomasi algorithm). OpenCV provides another algorithm to find the dense optical flow.
YouTube Excerpt: Pixel level movement in images - Dr Andy French takes us through the idea of Optic or
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