Embed Size (px)
Citation preview
A Statistical Approach to
Culture Colors Distribution in
Video SensorsAngela D’Angelo, Jean-Luc Dugelay
VPQM 2010, Scottsdale, Arizona, U.S.A, January 13-15
Outline
Introduction
Proposed approach
Colors Database
Visual Analysis
Fuzzy cluster
Conclusion
VPQM, Scottsdale, Arizona, U.S.A, January 13-15, 2010
Introduction
Color is an important cue in the distinction and
recognition of objects
Object recognition and identification
Image retrieval
Skin detection
Color recognition in video surveillance
Identifying the surface color of an object in a video
surveillance system is critical
• video sensors
• illumination conditions
• objects distances from the camera
• objects orientations
VPQM, Scottsdale, Arizona, U.S.A, January 13-15, 2010
Proposed work
Analysis of the performance of color spaces in
identifying colors in camera networks
Robust color identification tool
• Track a given person across the field-of-view of multiple
cameras
• Localize a missing child in a crowded amusement park
Related works
Color constancy algorithms
Color invariant models
VPQM, Scottsdale, Arizona, U.S.A, January 13-15, 2010
Color constancy algorithms
Color constancy is the ability of HVS to describe colors
in spite of variations in illumination conditions
Recover a descriptor for each surface in a scene as it
would be seen under a canonic illuminant taken as a
reference.
Common assumptions
Single camera
Uniform scene illumination
Frontal surface orientation
Spatially-distributed surveillance cameras operating
under different lighting conditions and with varying
color sensitivity
VPQM, Scottsdale, Arizona, U.S.A, January 13-15, 2010
Color invariant models
Color spaces invariant to illumination variations
Many color spaces and distance metrics have been
introduced in the scientific literature
Most of the paper does not provide a comparison of
the existing color spaces
Acceptable results on limited database with almost any
color space
Experimental results linked to a specific application
Skin color detection
VPQM, Scottsdale, Arizona, U.S.A, January 13-15, 2010
Culture colors
The goal of the proposed work is to learn how colors
can drift in different illumination conditions and with
different color spaces
Color selection
Culture colors (black, white, red, yellow, green, blue,
brown, purple, pink, orange, grey)
The idea is to collect pixels corresponding to the
culture colors, in real illumination conditions
VPQM, Scottsdale, Arizona, U.S.A, January 13-15, 2010
Mining testbed
Colors database
Associate each culture color to a sport team
Randomly collect video clips of the selected teams and
collect pixels from them
4/5 video clips for each team, around 120 pixels for each
color
VPQM, Scottsdale, Arizona, U.S.A, January 13-15, 2010
Mining testbed
1355 pixel samples
real illumination conditions
different positiond of the objects with respect to
illumination
different cameras
5 widely used color spaces: RGB, normalized RGB, HSV,
Lab, YUV
Analysis of the colors distributions in the 5 color spaces
VPQM, Scottsdale, Arizona, U.S.A, January 13-15, 2010
Colors distributions: HSV color space
Mettere i grafici dei piani hv hs
VPQM, Scottsdale, Arizona, U.S.A, January 13-15, 2010
Fuzzy clustering
Fuzzy clustering is suited to color quantization since
color boundaries are not well defined
Fuzzy k-nearest neighbors algorithm (Keller & al.)
Training set of m samples Z1, Z2, …, Zm
X vector to be classified
• Fixed a value of k and C (possible classes)
• Find among Z the k nearest neighbors to X: Y1, Y2, …,Yk
• Find the membership vector of X by combining the membership
vectors of Y
New dataset of 1104 samples
k
j
m
j
k
j
m
j
ij
i
YX
YXw
Xu
1
1
2
1
1
2
1
1
jiij Yuw
VPQM, Scottsdale, Arizona, U.S.A, January 13-15, 2010
Experimental results
ROC - RGB color space ROC - Normalized RGB color space ROC - HSV color space
ROC - Lab color space ROC - YUV color space
Tru
e P
osi
tive
Ra
te
Tru
e P
osi
tive
Ra
te
Tru
e P
osi
tive
Ra
te
Tru
e P
osi
tive
Ra
te
Tru
e P
osi
tive
Ra
teFalse Positive Rate False Positive RateFalse Positive Rate
False Positive Rate False Positive Rate
RGB Norm RGB HSV Lab YUV
Accuracy 91.3 89.9 86.8 94.1 91.4
VPQM, Scottsdale, Arizona, U.S.A, January 13-15, 2010
Conclusions
New approach to provide a comparison of the color
spaces in describing and identifying colors in video
Ad-hoc dataset
Visual analysis of colors distributions
Fuzzy clustering applied to 5 color spaces
Development of robust color detection framework
VPQM, Scottsdale, Arizona, U.S.A, January 13-15, 2010
Thanks for the attention!
