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Intelligent video monitoring for anomalous event detection.
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I. Gómez-Conde, D. Olivieri, X.A. Vila Sobrino, A. Orosa-Rodríguez
(University of Vigo)
Salamanca (6-8th April, 2011)
Intelligent Video Monitoring for Anomalous
Event Detection
www.milegroup.net
• Introduction
• Our approach
o Software algorithms for the tele-assistance for the elderly
oMultiple object tracking techniques
oBehavior detectors based on human body positions
• Experimental Results
• Conclusions
Index
Iván Gómez Conde
o % people (65 years and over)
o % youth (under 15 years)
o In 2050, % elderly people % youth
o Problems:
Sociologic
Economic
Computer Vision can be used as early warning monitor for anomalous event detection!!!
The aging of the population has increased dramatically.
Current problem
Iván Gómez Conde
The motivation for this paper is the development of a tele-assistance application.
Detect foreground objects
Track these objects in time
Action Recognition
Motivation
Iván Gómez Conde
o Image analysis
o Machine learning
o Transate the low level signal to a higher semantic level
o Inference actions and behaviors
Present computer aplications go far beyond the simple security camera of a decade ago and now include:
What is the monitoring?
Iván Gómez Conde
Method for comparing foreground- background segmentation
Feature vector tracking algorithm
Simple real-time histogram based algorithm for discriminating movements and actions
There are several original contributions proposed by this paper:
Contributions
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• C++
• OpenCV (Open Source Computer Vision)
Qt
Octave
Software
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System
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This software is an experimental application. The graphical interface provides maximum information.
Detecting movement
There are several background subtraction methods. We use two methods:
• Running Average
• Gaussian Mixture Model
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Running Average
A = Matrix of accumulated pixels
I = Image
Nf = nº of used frames
α = weighting parameter Є [0,1]
Each point of the background is calculated with the mean of the backgrounds over Nf previous frames.
At(Nf) = (1-α) At-1(Nf) + α It
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Running average
Iván Gómez Conde
Gaussian Mixture Model
This method models each background pixel as a mixture of K Gaussian distributions
o K is tipically from 3 to 5
o Eliminates many of the artefacts that Running Average is unable to treat
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Gaussian Mixture Model
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Testing Methods (% error)
FN + FP 640∙480
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• False Negatives (FN): Foreground pixels labeled as background
• False Positives (FP): Background pixels labeled as foreground
% error =
Finding individual objects
• Foreground objects rectangular “blobs”
detect blob
while (∃ blob) do
apply mask
create color histogram
aproximate with gauss
create feature vector
detect new blob
end while
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Feature vector for classification
Feature Vector
Size and coordinates of the blob center
Gaussian fitted values of RGB components
Motion vector
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Discrimination objects
Norm difference of red channel
Nor
m d
iffe
ren
ce o
f g
reen
ch
an
nel
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Tracking algorithm
Once objects have been separated and characterized by their feature vector, we tracks
Tracking is performed by matching features of the rectangular regions
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Tracking algorithm
• Position from t to t+1 (x = xo + vt)
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Time chart
Bg-Fg Seg. Blob Detection Normal Video Video with Qt
Frame 1 28.3 ms 168.5 ms 33.2 ms 2.5 ms
Frame 30 847.5 ms 5065.4 ms 997.2 ms 75.82 ms
Frame 361 10198.2 ms 60954.1 ms 12000 ms 912.36 ms
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Detecting gestures We have considered a limited domain of events
Discrimination arms gestures
o The mass histogram
o Statistical moments
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Detecting actions
Normalized Histogram
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• Basic body position
o Upright
o Lying down
• The inset image is the histogram normalized to unity
Discrimination actions
Figure 1 Figure 2 Figure 3
µ 0.54 0.33 0.44
σ 0.21 0.17 0.21
µ3
0.17 3.99 3.12
Iván Gómez Conde
Conclusions
Our software aplication will allow track people and discriminate basic actions
The system is actually part of a more complete tele-monitoring system
The paper opens many possibilities for future study.
o Using our quantitative comparison to optimize parameters
o Combining feature vector with sequential Monte Carlo methods
Iván Gómez Conde
Conclusions
The histogram model developed in this paper provides detection for a limited set of actions and events:
Real-time method
Easy to implement
Should have utility in real systems
It is not sufficiently robust
Iván Gómez Conde
Many thanks for your attention
Iván Gómez Conde