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

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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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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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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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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

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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

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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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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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Many thanks for your attention

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