Optical Flow

1 / 11

# Optical Flow - PowerPoint PPT Presentation

Optical Flow. 10-24-2005. Problem. Problems in motion estimation Noise, color (intensity) smoothness, lighting (shadowing effects), occlusion, abrupt movements, etc Approaches: Block matching, Generalized block matching, Optical flow (block-based, Horn-Schunck etc) Bayesian, etc.

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

## PowerPoint Slideshow about ' Optical Flow' - dionysus-dale

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

### Optical Flow

10-24-2005

Problem
• Problems in motion estimation
• Noise,
• color (intensity) smoothness,
• occlusion,
• abrupt movements, etc
• Approaches:
• Block matching,
• Generalized block matching,
• Optical flow (block-based, Horn-Schunck etc)
• Bayesian, etc.
• Applications
• Video coding and compression,
• Segmentation
• Object reconstruction (structure-from-motion)
• Detection and tracking, etc.

=

ì

x

X

í

=

y

Y

î

Motion description
• 2D motion:
• p = [x(t),y(t)]p’= [x(t+ t0), y(t+t0)]
• d(t) = [x(t+ t0)-x(t),y(t+t0)-y(t)]
• 3D motion:
• Α= [ Χ1, Υ1, Ζ1 ]ΤΒ = [ Χ2, Υ2, Ζ2 ]Τ
• = R+T
• Basic projection models:
• Orthographic
• Perspective
Optical Flow
• Basic assumptions:
• Image is smooth locally
• Pixel intensity does not change over time (no lighting changes)
• Normal flow:
• Second order differential equation:

and

Block-based Optical Flow Estimation
• Optical flow estimation within a block (smoothness assumption): all pixels of the block have the same motion
• Error:
• Motion equation:

Gauss-Seidel

Horn-Schunck
• We want an optical flow field that satisfies the Optical Flow Equation with the minimum variance between the vectors (smoothness)