Tracking objects using Gabor filters
Download
Report
Transcript Tracking objects using Gabor filters
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
2
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.
Tracking objects using Gabor filters
3
Color spaces
RGB
rgb
HSV
Intensity
Tracking objects using Gabor filters
4
Gabor filters
Captures localized frequency information
Biologically motivated
Tracking objects using Gabor filters
5
1D Gabor
*
=
Tracking objects using Gabor filters
6
2D Log-Gabor
*
=
Tracking objects using Gabor filters
7
Inverse Fourier transform
IFFT ->
Tracking objects using Gabor filters
8
Feature selection
1D histograms
Background/object separation
Tracking objects using Gabor filters
9
Mean shift tracking
Probability density of target model and
target candidate
Try to minimize distance between
probability densities
Tracking objects using Gabor filters
10
Demonstration
Tracking objects using Gabor filters
11
Tracking without log-Gabor
12
Tracking without log-Gabor
13
Tracking a grid ball
14
Tracking using log-Gabor
Tracking objects using Gabor filters
15
Tracking a leaf
Tracking objects using Gabor filters
16
Tracking
without
log-Gabor
Tracking objects using Gabor filters
17
Tracking a leaf
18
Tracking
with logGabor
Tracking objects using Gabor filters
19
Tracking a soccer ball
Tracking objects using Gabor filters
20
Tracking without log-Gabor
Tracking objects using Gabor filters
21
Tracking a soccer ball
Tracking objects using Gabor filters
22
Tracking with log-Gabor
Tracking objects using Gabor filters
23
Problems
High wavelength log-Gabor filters
A frame in the soccer game movie
Tracking objects using Gabor filters
24
Problems
High wavelength filters pollute feature selection
Solved by limiting wavelength of log-Gabor filter to 2*min(h,w)
25
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
Tracking objects using Gabor filters
26
Questions?
Tracking objects using Gabor filters
27