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.
optical flow estimationcomputer vision
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