This document summarizes a paper on intelligent video monitoring for anomalous event detection. The paper proposes software algorithms using multiple object tracking techniques and behavior detectors based on human body positions to develop a tele-assistance application for the elderly. The approach uses background subtraction, foreground object detection, feature vector tracking, and histogram analysis to discriminate simple gestures and actions like upright, lying down positions. Experimental results show the system can track people and detect basic actions in real-time with limited accuracy for a small set of events. The paper concludes the method provides a starting point but is not robust enough for all real world applications.

1. 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

2. • Introduction
• Our approach
oSoftware algorithms for the tele-assistance for the elderly
oMultiple object tracking techniques
oBehavior detectors based on human body positions
• Experimental Results
• Conclusions
Index
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3. 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
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4.  The motivation for this paper is the development of
a tele-assistance application.
Detect foreground objects
Track these objects in time
Action Recognition
Motivation
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5. 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?
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6. 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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7. • C++
• OpenCV (Open Source
Computer Vision)
 Qt
 Octave
Software
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8. System
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 This software is an experimental application. The
graphical interface provides maximum information.

9. Detecting movement
 There are several background subtraction methods.
We use two methods:
• Running Average
• Gaussian Mixture
Model
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10. 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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11. Running average
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12. 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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13. Gaussian Mixture Model
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14. 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 =

15. 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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16. 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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17. Discrimination objects
Norm difference of red channel
Normdifferenceofgreenchannel
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18. 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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19. Tracking algorithm
• Position from t to t+1 (x = xo + vt)
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20. 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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21. Detecting gestures
 We have considered a limited domain of events
 Discrimination arms gestures
o The mass histogram
o Statistical moments
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22. 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

23. 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
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24. 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
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25. 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
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26. Many thanks for your
attention
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