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# 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.

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

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Presentation Transcript

### Optical Flow

10-24-2005

• 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

• Basic assumptions:

• Image is smooth locally

• Pixel intensity does not change over time (no lighting changes)

• Normal flow:

• Second order differential equation:

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:

Horn-Schunck

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