Tracking objects
using Gabor filters
Mark de Greef, Sjoerd Kerkstra, Roeland Weve
Supervisor: Theo Gevers
Overview
Introduction
 Color spaces
 Feature selection and mean shift
 Demonstration
 Conclusion

Tracking objects using Gabor filters
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Introduction

Goal: investigate the use of texture in tracking.

Trackers often use color features (like RGB)

Color features are not sufficient for tracking in some
cases

We use texture information as a basis for tracking

Our tracker is based on the on-line feature selection
framework.
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Color spaces




RGB
rgb
HSV
Intensity
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Gabor filters

Captures localized frequency information

Biologically motivated
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1D Gabor
*
=
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2D Log-Gabor
*
=
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Inverse Fourier transform
IFFT ->
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Feature selection


1D histograms
Background/object separation
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Mean shift tracking

Probability density of target model and
target candidate

Try to minimize distance between
probability densities
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Demonstration
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Tracking without log-Gabor
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Tracking without log-Gabor
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Tracking a grid ball
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Tracking using log-Gabor
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Tracking a leaf
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Tracking
without
log-Gabor
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Tracking a leaf
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Tracking
with logGabor
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Tracking a soccer ball
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Tracking without log-Gabor
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Tracking a soccer ball
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Tracking with log-Gabor
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Problems

High wavelength log-Gabor filters
A frame in the soccer game movie
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Problems

High wavelength filters pollute feature selection
Solved by limiting wavelength of log-Gabor filter to 2*min(h,w)
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Conclusion
Log-Gabor filters make tracking possible in
situations where color features do not
 Problems with large wavelengths can be
avoided
 Addition of log-Gabor features to color
features does not degrade tracking
performance

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Questions?
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