Optical Flow
Computing motion vectors between consecutive video frames showing where each pixel moves.
Optical flow computes pixel movements between video frames – foundation for slow motion, video stabilization, action recognition, and autonomous driving.
Explanation
Optical flow captures apparent motion in an image sequence. Methods range from classical (Lucas-Kanade, Farneback) to deep learning (RAFT, FlowNet).
Marketing Relevance
Optical flow is fundamental for video analysis, action recognition, video stabilization, and autonomous driving.
Example
A video editor uses optical flow for slow-motion interpolation – missing frames are synthesized from motion vectors.
Common Pitfalls
Errors at occlusions and large motions. High compute for dense flow. Illumination changes disrupt classical methods.
Origin & History
Horn-Schunck (1981) and Lucas-Kanade (1981) laid the mathematical foundations. FlowNet (2015) brought deep learning. RAFT (2020) set new state-of-the-art accuracy with recurrent architecture.
Comparisons & Differences
Optical Flow vs. Object Tracking
Optical flow computes dense pixel motion. Object tracking follows specific objects across frames (sparser but semantic).
Optical Flow vs. Depth Estimation
Optical flow captures 2D motion over time. Depth estimation predicts 3D distance in a single frame.