Summary
Things to take away from this lecture
•Optical flow problem definition
•Aperture problem and how it arises
•Assumptions
–Brightness constancy, small motion, smoothness
•Derivation of optical flow constraint equation
•Lukas-Kanade equation
–Derivation
–Conditions for solvability
–meanings of eigenvalues and eigenvectors
•Iterative refinement
–Newton’s method
–Coarse-to-fine flow estimation
•Feature tracking
–Harris feature detector
–L-K vs. discrete search method
–Tracking over many frames
–Prediction using dynamics
•Applications
–MPEG video compression
–Image alignment