AN EFFICIENT SENSOR FOR TRAFFIC MONITORING AND

TRACKING APPLICATIONS

Based on Fast Motion Detection at the Areas of Interest

Nikolaos Zournis-Karouzos

Deartment of Electrical and Computer Eng.,Aristotle Univ. of Thessaloniki,Thessaloniki, Greece

Alexandra Koutsia, Kosmas Dimitropoulos, Nikos Grammalidis

Informatics and Telematics Institute, CERTH, 1

km Thermi-Panorama Rd, Thessaloniki, Greece

Keywords: Motion detection, traffic monitoring, target tracking, background extraction and update, A-SMGCS.

Abstract: We propose a novel video sensor for real-time motion detection at specific user-defined regions of interest,

designed primarily for traffic monitoring, surveillance and tracking applications. Specifically, the new

sensor a) supports virtual detectors with a generalized (polygonal) shape, thus providing additional

flexibility in the design of detector configurations, b) is based on fast implementations of recent state-of-the

art background extraction and update techniques and c) constitutes a generic, inexpensive software solution,

which can be used with any video camera. First experimental results confirm that the new video sensor

meets the expectations in terms of real-time performance and demonstrates the additional functionalities,

according to which it was designed. The final goal is to use this new sensor as an alternative, improved

version of embedded motion detection video sensors (like Autoscope®).

1 INTRODUCTION

Within the last years, there is increased market

demand for the development of efficient automated

systems that use computer vision techniques for real-

time traffic monitoring, surveillance and accident

control. These systems have also been used to

augment existing Advanced Surface Monitoring,

Guidance and Control Systems (A-SMGCS), (ICAO

document, 1986) at airports (Besada et al, 2005),

(Pavlidou et al, 2005).

An example of such a system is Autoscope®

Solo Wide Area Video Vehicle Detection System.

However, such systems are usually very expensive,

since they use specialized cameras with additional

integrated (onboard or not) hardware/sofware for

real-time motion detection. Furthermore, they are

not very efficient if used as sensors for tracking or

security surveillance applications. In the FP5 IST

INTERVUSE and FP6 EMMA projects (Pavlidou et

al, 2005), Autoscope sensors were successfully used

to provide an alternative A-SMGCS solution for

small-medium airports without any A-SMGCS

means or to augment an existing A-SMGCS system

(typically based on a surface radar) by covering

specific “blind spots” (usually occurring near

buildings or other obstacles).

However, specific shortcomings were

identified: a) constraints due to the rectangular

nature of virtual detectors, b) use of older, traditional

image processing algorithms and c) the high cost of

video sensors. This paper proposes a novel system

for real-time motion detection at specific regions of

interest within the camera’s field of view, which

aims to avoid the above shortcomings. Specifically,

it a) is using virtual detectors of a generalized

polygonal shape, b) is based on fast implementations

of recent state-of-the art background extraction and

update techniques and c) is inexpensive, being

implemented entirely in software. First experimental

results confirm that the new video sensor meets the

expectations in terms of real-time performance. The

final future goal is to use this new sensor as an

alternative, improved version of the Autoscope

video sensors for the targeted applications.

The rest of this paper is organized as follows:

In Section 2, a brief introduction is made to the

666

Zournis-Karouzos N., Koutsia A., Dimitropoulos K. and Grammalidis N. (2008).

AN EFFICIENT SENSOR FOR TRAFFIC MONITORING AND TRACKING APPLICATIONS - Based on Fast Motion Detection at the Areas of Interest.

In Proceedings of the Third International Conference on Computer Vision Theory and Applications, pages 666-669

DOI: 10.5220/0001084006660669

 SciTePress

Autoscope Vehicle Detection and its use withing the

INTERVUSE project. Section 3 presents the four

background extraction and update techniques

extended in this paper to provide fast and reliable

motion detection for the generalized-shape

(polygonal) virtual detectors. Finally, Section 4

contains experimental results and conclusions

demonstrating the computational gains achieved by

the proposed technique.

2 THE AUTOSCOPE VEHICLE

DETECTION SYSTEM

The Autoscope® Solo Wide Area Video Vehicle

Detection System is an advanced, sophisticated,

traffic surveillance system that uses machine vision

technology to produce highly accurate traffic

measurements (Image Sensing System, 2007),

(Michalopoulos et al, 1993). It is used for traffic

control centres and Internet information systems as

well as incident detection to improve emergency

response times of local authorities.

The Autoscope camera has a built-in Machine

Vision Processor (MVP) which provides many

benefits such as: a) there is no need for high

bandwidth video transmission between the camera

and the MVP, b) enables closed loop control of the

camera optics such as illumination, gain, brightness

and electronic zoom by the vision processor itself, c)

makes the system more easily portable.

Autoscope cameras are addressable by a unique

IP address and can be linked to each other, as well as

to a PC for configuration and statistics collection,

using RS-485 communication.

Each camera can detect traffic in multiple

locations within its field of view. Rectangular areas,

called virtual detectors (VDs), can be defined by the

user on the camera image plane, each corresponding

to a binary output. More complex virtual detectors

can also be defined by combining detector outputs

by means of logical and mathematical expressions

(AND, OR, NOT, time based consideration,

averages, sums, etc). The main advantage of the use

of virtual detectors is that processing involves only

the pixels of the specified areas of the image, thus

reducing the computational requirements.

Once the location of virtual detectors has been

specified, the background in the absence of vehicle

is estimated. Virtual detectors detect the presence of

vehicles by estimating the statistics of the

background from which a threshold is determined.

Then the instantaneous image pixel values are

compared with this threshold and if they are greater

it means that a vehicle is present (Michalopoulos,

1991). Over time, the inbuilt pattern recognition

software learns pattern of contrast, thus very well

coping with night, fog, snow and rain, as experience

with road traffic has shown.

Within the INTERVUSE project (Pavlidou et

al, 2005) this detection system was used for

monitoring of airport ground traffic. More

specifically, information from all available virtual

detectors configured in the video sensor network is

continuously provided to Video Sensor Data Fusion

(VSDF) server through a polling procedure. The

VSDF server then processes these data in order to

extract observations (measurements or plots).

Observations contain information about the

estimated position and size of targets and the date

and time of detections. These observations are sent

to the tracker of the system for further processing.

Ground coordinates corresponding to each fused

observation are obtained using a calibration

procedure, which is performed as a pre-processing

step. It is assumed that the 3-D structure captured by

each camera can be modelled as a plane, which is

approximately true for most airport (and even road)

applications.

3 BACKGROUND EXTRACTION

TECHNIQUES AND POLYGONS

For the reliable detection of moving targets in the

field of view of each camera, the estimation of the

background and its periodic update are required.

This is a very demanding problem especially for

outdoor environments, where external factors such

as camera oscillations, weather, gradual or sudden

illumination changes and/or movements of objects

belonging to the background affect the detection of

moving targets. Such problems are usually addressed

either by techniques aiming to update automatically

the background (Gupte et al, 2002) (e.g. by taking a

weighted average of the current background and the

current frame of the video sequence) or by complex

techniques (Borg et al, 2005), which apply statistical

models for the estimation of each pixel value (e.g.

mixture of Gaussians (Stauffer and Grimson, 1999),

colour and edge fusion method (Jabri et al, 2000)

etc).

In this paper, four state-of-the-art background

modelling, subtraction and update techniques were

extended so that they are applied only within

specific regions of interest, defined by a set of

AN EFFICIENT SENSOR FOR TRAFFIC MONITORING AND TRACKING APPLICATIONS - Based on Fast Motion

Detection at the Areas of Interest

667

polygon-shaped detectors. The four methods are the

Bayes technique (Li et al, 2003), the mixture of

Gaussians (KaewTraKulPong and Bowden, 2001),

the reliable background subtraction and update

(Lluis et al, 2005) and finally, the non-parametric

model for background subtraction (Elgammal et al,

2000). These extensions are seen to result to a very

significant reduction of the complexity and

execution times, as demonstrated in the experimental

results section. Therefore, even techniques with

increased computational complexity, like the Bayes-

based or the Non-parametric Model approaches can

be considered suitable for integration in real-time

systems using the proposed technique. A sample

foreground mask for each of the four methods is

shown in Figure 1.

Bayes Gauss

Lluis Non-parametric

Figure 1: Foreground masks for the four methods.

Autoscope’s concept of limiting the application

of background extraction techniques to rectangular

areas is an effective way of reducing execution time.

However, it also limits the ability of the user to

design more efficient detector configurations. To

solve this problem, general polygon-shaped VDs

were supported in the proposed approach.

Furthermore, an additional “Sensitivity Indicator”

property was added to each VD, in order to make the

motion detection system even more flexible.

An off-line tool called “PolyMapper” was

implemented to allow the user to define polygons of

any shape and size depending on the scene structure

and their specific needs and to adjust a threshold

(sensitivity indicator) for each polygon, giving the

percentage of pixels that have to be part of the

foreground for the sensor to be considered as active.

PolyMapper was built using the Qt library and can

run both under Windows and Linux.

A sample frame and mask with the polygonal

sensors marked is shown in Figure 2. When the

percentage of foreground pixels is over a threshold,

the sensor is highlighted.

Figure 2: Sample frame and mask with polygonal sensors.

4 RESULTS AND CONCLUSIONS

For the purposes of this paper, the use of the new

sensor was tested on traffic sequences with three

different resolutions (320x740px, 640x480px,

768x576px). To have a more quantitative view of

the time gain achieved with the polygon sensors, the

four methods were applied on 50 frames of all three

sequences and the frame rates achieved can be seen

Figure 3 for both the entire picture and the

polygonal areas. These results do not include the

time of the frame capturing process. For these tests,

the methods were implemented using C++ and the

OpenCV library. The system used was an Intel

Pentium 4 3.2GHz with 1GB of RAM running on

Windows XP Pro.

Finally,

Figure 4 illustrates the percentage of

decrease in execution times accomplished for the

specific sequences.

frame rates

100

120

320x240

640x480

768x576

320x240

640x480

768x576

320x240

640x480

768x576

320x240

640x480

768x576

bayes gauss lluis np

frames per sec

entire picture

polygons

Figure 3: Test results, chart of frame rates.

The use of polygonal sensors to monitor traffic is

proved to be notably effective. The execution times

of modern but time consuming algorithms were

decreased, allowing for use in real time applications.

VISAPP 2008 - International Conference on Computer Vision Theory and Applications

668

Decrease in execution time

100

320x240

640x480

768x576

320x240

640x480

768x576

320x240

640x480

768x576

320x240

640x480

768x576

bayes gauss lluis np

%decrease in execution time

Figure 4: Test results, chart of decrease in execution times.

Moreover, the polygonal shape gives flexibility to

monitor areas that could not be covered with

orthogonal sensors and the sensitivity indicator

provides a way to parameterize each sensor

separately, according to the user needs.

The performance of the four background

extraction methods was also evaluated. The Bayes

method, although it benefits from the proposed

technique, does not provide satisfactory results in

cases of slowly moving targets and it still remains

quite slow. The Gauss method is faster but is not

suitable for outdoor scenes, since it has problems

coping with shadows. Results from the Lluis method

deteriorate as the sequence resolution is increased.

Finally, the non-parametric model method which

provides the best foreground masks, benefits a lot

from this technique, thus, it can be considered for

real time applications.

In general, the obtained results are very

promising and show great potential for the new

sensor to be integrated as an alternative that can

replace the Autoscope sensor for target tracking

applications, similar to those developed by

INTERVUSE project. Hardware implementations of

the new algorithms may further reduce the

computational costs and allow for the production of

embedded systems such as Autoscope.

ACKNOWLEDGEMENTS

This work was supported by the General Secretariat

of Research and Technology Hellas under the

InfoSoc “TRAVIS: Traffic VISual monitoring”

project and the EC under the FP6 IST Network of

Excellence: “3DTV-Integrated Three-Dimensional

Television - Capture, Transmission, and Display”

(contract FP6-511568).

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