optical flow estimation

Optical Flow Estimation is the task of calculating the apparent motion of image brightness patterns — determining a displacement vector $(u, v)$ for every pixel between two consecutive video frames, representing how pixels "move" over time.

What Is Optical Flow?

• Definition: Dense 2D motion field.
• Assumption: Brightness Constancy (the pixel's color doesn't change, it just moves).
• Output: A color-coded map where color indicates direction and intensity indicates speed.

Why It Matters

• Video Compression: "This block just moved 5 pixels left", saving massive bandwidth (MPEG).
• Stabilization: Smoothing out shaky camera footage.
• Action Recognition: Two-stream networks use flow to "see" motion explicitly.

Key Models

• Classical: Lucas-Kanade, Horn-Schunck.
• Deep Learning: FlowNet, PWC-Net, RAFT (Recurrent All-Pairs Field Transforms).

Optical Flow Estimation is pixel-level motion tracking — the foundational signal processing step that underpins most modern video analysis algorithms.