Applications of Video Surveillance Systems for Traffic

Data Acquisition

Vasil Lakov

University of Architecture, Civil Engineering and Geodesy

1046 Sofia, Bulgaria

Abstract. The process of urbanization raises city population and extends city

boundaries, which leads to heavier road traffic, environmental pollution and

bigger financial losses. Due to spatial structure and influence of many factors

on transport conditions, the transport system requires complex management

methods. To be widely adopted, traffic management projects have to have the

lowest possible price and highest effect on transportation. To accomplish these

requirements, city authorities have to know what and where the halting points

are. Using existing CCTV cameras for traffic purposes can empower authorities

to easily obtain city-wide traffic data. This paper analyzes the benefits and

problem areas of CCTV systems when used for traffic surveillance and present

different ways for improving reliability and accuracy of these systems. Exam-

ined areas are camera settings, optical filters, mounting place, digital image

processing techniques for image enhancing and object extraction and objects

filtering based on their properties.

1 Introduction

The rising of the world population and the process of urbanization are some of the

reasons for differentiation of densely populated city areas. The growth of cities in

both horizontal and vertical direction, strengthening of economic links among them

and constant searching for profit require more effective ways for people and goods

transportation. Road transport arteries connecting residential with industrial and

commercial city areas, and highways between cities, gather extremely important role

in functioning and growth of every modern country.

Mass usage of automobile transport is constantly increasing since its biggest ad-

vantage is door-to-door service. To be able to guarantee normal functioning of the

global economical system, road transport systems must be:

− efficient (providing alternative routes, enough capacity, suitable for all vehicles),

− effective (with minimal expense of time and fuel),

− reliable (with minimal disturbance from planned and random events),

− ecological (with minimal environmental pollution),

− safe (with minimal risk for traffic participants).

Lakov V. (2011).

Applications of Video Surveillance Systems for Trafﬁc Data Acquisition.

In Proceedings of the 1st International Workshop on Future Internet Applications for Trafﬁc Surveillance and Management, pages 102-113

DOI: 10.5220/0004473501020113

 SciTePress

As reasons for traffic jams, and the ways of reducing them are complex. The ways for

improving transport quality may be grouped in the following categories:

− restricting or stimulation of using one or another type of vehicles and traffic reor-

ganization,

− city areas and roads planning consistent with current and future transport needs,

− renewal of old one and constructing of new road infrastructure,

− enhancing of traffic flows surveillance and control.

2 The Role of Traffic Sensors

Intelligent Transportation Systems (ITS) are in use worldwide nowadays and their

aim is to improve safety, performance, environmental impact and to provide sustain-

able development of road transport systems by modern transport technologies.

For identifying the bottlenecks and their locations, as also for revealing and evalu-

ating the possible corrective actions, it is mandatory to have information about the

road infrastructure and the traffic flows. The realization of a specific traffic project

can influence different aspects of transport conditions and evaluating the project

consequences in advance is an important step in its planning. By creating computer

models of city or national road networks and by using real stress data acquired by

traffic sensors optimal decisions are more easily taken in various situations during

traffic management and planning in response to current demand and conditions [1,2].

Traffic flow management relies on up-to-date data for different traffic parameters.

In addition to individual vehicle parameters sensors compute generalized indicators

describing automobile flow as a whole. This data is used for a wide range of tasks

such as:

− evaluation of road load,

− simulation of computer transport models,

− adaptive control of traffic lights,

− identifying of peek hours,

− identifying locations with frequent traffic congestions,

− evaluation of sound and atmospheric pollution,

− determination of the predominant types of vehicles,

− route determination of major traffic flows.

Examples of measured vehicle parameters are:

− used lane,

− direction,

− speed,

− type of vehicle,

− weight of the vehicle,

− length of the vehicle,

− time of passage,

− registration number,

On the basis of these parameters following indicators can be computed:

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− average flow speed,

− distance between consecutive vehicles,

− loading degree of the road section,

− air and noise pollution,

− existing or impending congestion,

− traffic violations.

For the realization of the most suitable project meeting expected improvement of

transportation conditions it is necessary to clarify beforehand the following questions:

− what is the transport problem,

− what information is needed for solving the problem,

− what are the specifics of the road section and the traffic.

Having defined the above points a project may proceed with the choice of technology

and specific road sensors meeting the requirements.

3 Traffic Sensors

The two main groups of traffic sensors are:

− in-vehicle sensors,

− road sensors.

The first group of sensors represents devices such as mobile phones, GPS receivers

and RFID transponders, mounted in each vehicle and monitoring only this vehicle. In

this case, many devices are used for ensuring proper data and covering larger area of

the road network.

The second group of sensors is installed at a specific point of the network for

measuring local traffic parameters only. One road sensor can monitor multiple lanes

with the same or different directions. This group includes video surveillance systems

for acquiring and analyzing traffic data. These are passive sensors covering long road

section, providing simultaneously video signal from the place, which is the major

difference compared to other types of road sensors. For extracting useful traffic data

from the video signal different algorithms for video image processing are being ap-

plied [2].

4 Video Road Sensors

Video road sensors are based on video surveillance systems and techniques from

machine (computer) vision for analyzing of video frames in the process of acquiring

data for the vehicles visible in the frames. The video camera frames can be processed

either centralized in the control centre or locally by each video camera.

In the first case, computers are located in the data centre and are processing video

streams from remote cameras, as one computer or controller can handle multiple

video streams. In the second case, video cameras are equipped with integrated com-

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puter modules for digital image processing. These video cameras transmit only traffic

data to the control centre, but it is also possible to stream live video. The choice of the

video system type depends on various factors, whereas it is possible to establish a

hybrid system including already installed analogue cameras as well as new smart

digital cameras. Streaming of the video signal from the cameras can be permanently

or when certain conditions are being registered such as abnormal parameter values, a

traffic incident or a violation [3].

Video road sensors implement two types of algorithms for measuring vehicle pa-

rameters. The first one measures parameters only once in a set point of the video

frame, normally when vehicle crosses a virtual line, thus simulating physical road

sensors. The other type measure constantly vehicle parameters, as long as vehicles are

visible. This process includes tracking each vehicle on each consecutive video frame.

In order to be appropriate for road traffic monitoring, video road sensors must ful-

fil following requirements:

− automatic detection of all vehicles,

− detection of both moving and stopped vehicles,

− operability under various weather conditions,

− operability in real time.

To be considered a universal tool in transportation projects video road sensors must

also provide:

− high accuracy of the measured parameters,

− simplicity of installation and maintenance,

− high fault tolerance,

− ability to exchange information with other systems,

− ability to visualize and analyze collected data.

5 Advantages of Video Sensors

In the big cities, there are already established systems for video surveillance of vari-

ous public places, including important road arteries for the purpose of security. The

signal from these video cameras can be used simultaneously for the purpose of traffic

monitoring and safety. In this case the whole infrastructure is ready and necessary

investments are substantially lower. What remains to be done is the installation of

computer equipment in data centres for processing video signals from cameras. This

significantly reduces the time for building the system.

With the ability to cover a large number of lanes and a long road section by one

video sensor different road situations can be detected e.g.:

− speeding,

− stopped or parked vehicles,

− driving in forbidden lane,

− driving in wrong direction,

− crossing a continuous line,

− red light crossing,

− smoke in tunnels,

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− debris on the road.

This allows obtaining extensive information about the traffic by installing a small

number of devices. Installation of new cameras can be done on the side of the road, as

not to hinder normal traffic.

Adding new features to the processing of the video signal can be done in stages,

starting with vehicle counting and completing the system with their classification and

violations and congestion monitoring. The modular structure of the software system

will further accelerate the introduction of the system in operation and will allow the

creation of optimized solutions for every specific project. The general use of video

cameras eliminates the need to install other cameras for obtaining video images used

by supervisory authorities to assess the traffic situation.

Thanks to the digital nature of the video sensor functioning and modern communi-

cations, it is possible to analyze video signals from anywhere in the world. To reduce

needed bandwidth of the communication system, it is advisable to send first only a

picture and after the situation has been assessed by operator to begin video broadcast

in real time.

6 Disadvantages of Video Sensors

Difficulties in the operation of video sensors are a consequence of the difficulty in

extracting the necessary data from the video footage in degraded frame quality,

caused by the following factors:

− low light and bad weather conditions,

− low quality of video frames,

− scene complexity.

6.1 Low Light and Bad Weather Conditions

Insufficient light on the road is a major problem for video sensors. Separating cars

from the background becomes unreliable, which leads to missed cars. Many algo-

rithms for detecting cars do not work as well at night.

Bad weather such as heavy rain, snow and fog reduces the road lighting and image

contrast and may introduce noise in the frame. Visibility is also reduced and the rec-

ognition of distant vehicles becomes difficult. The algorithms which use specific

points or object edges for identifying and tracking of vehicles will not be able to

reliably detect vehicles due to reduced contrast. Reduced number of the contours in

the image as a result of decreased contrast is used by some systems for smoke detec-

tion in tunnels.

6.2 Low Quality of Video Frames

The quality of video frames is influenced by the following video camera elements:

− camera lens,

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− image sensor type,

− automatic video functions.

Optical distortions are defects caused by a simple single lens or other imperfect opti-

cal system due to some phenomena and geometrical characteristics of light passing

through each lens. Each lens suffers, to some extend, from various optical defects. As

a result of these effects, the image created by the optical system is poorly focused,

distorted or with changed colours. Distortions are of several types and are removed

by a combination of different lenses. Completely removal of all distortions is impos-

sible, and only these distortions that hinder the specific task are removed. This is

done through complex combinations of lenses of different shapes, made of glass with

different refraction ratios and combined in a way so that to remove all undesired

image faults. Compensating the various distortions is necessary for the proper meas-

urement of vehicle speed, trajectory determination and classification [4].

A smaller photosensitive element creates a stronger noise in the image and if the

frame resolution is also low, the details in the image become indistinguishable, which

worsens the overall accuracy of the extracted data.

CCD and CMOS sensors represent two different technologies for capturing digital

images. Each has its strengths and weaknesses depending on the application and nei-

ther of the two is clearly superior to the other. For the purpose of traffic monitoring

the frame speed is the one of the standard TV and the time for digitalization of the

frame is not so important. But it is important to minimize the noise, keep the colour

uniformity and light sensitivity at high levels because traffic cameras work 24/7.

Characteristics of both technologies are shown in table 1.

Table 1. Characteristics of image sensors.

Indicator CCD CMOS

Chip output Voltage (analogue) Bits (digital)

Number of amplifiers One For each pixel

Noise level Low Medium

Complexity Low High

Dynamic range High Medium

Colour uniformity High Low to medium

Conversion speed Medium to high Higher

Power consumption High Low

Heating High Low

Pixels density High Medium

CCTV cameras have a set of functions for automatic determination of parameters for

frame capturing depending on ambient conditions. For road sections monitoring,

changes in the scene are small and slow, and the most important factor is the total

light during the day. Some functions are not needed and removing or disabling them

facilitates the processing and reduces the cost of the camera. These unwanted func-

tions are BLC (Back-Light Compensation), AWB (Automatic White Balance), AF

(Automatic Focus) and AI (Automatic Iris). In the absence of light sources behind the

road BLC should be blocked. The colour change with AWB can reduce the reliability

of the algorithms using colour information for detecting vehicles. AF and AI func-

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tions apply mechanical techniques which reduce the reliability of the camera and

increase the complexity and cost. After mounting the camera the focus remains un-

changed and the AI function is replaced by optical filters, AGC and AES.

6.3 Scene Complexity

When the video camera is mounted close to the ground or directed almost perpen-

dicular to the road, it is possible for nearby vehicles to get overlapped and even hid-

den one by another. This leads to wrong counts or incorrect parameter values of these

cars. Merging of close cars is possible also during periods when the sun is close to the

horizon. Then cast shadows are longer and may fall on adjacent cars [5].

The presence of moving objects near the roadway or of ones often passing through

it can also lead to the adoption of these objects by the processing algorithms as real

cars. In this case, false alarms of traffic violations or other dangerous situations are

more likely to be generated by the system. Such unwanted objects can be:

− wild animals,

− pedestrians,

− moving trees,

− buildings.

Trees located adjacent to the road may hide part of it or be source of movement in the

frame (moving cars) in the presence of wind. Light sources at night can blind the

camera, illuminate excessively part of the road or be recognized as car headlights.

Glare from road surface (especially when wet) or vehicles surfaces in some cases can

be the cause for the impossibility to determine the vehicle type, the fusion of cars and

road and the loss of colour information. Even if these unwanted objects are out of the

analyzed area of the frame, they can change various parameters used in frame proc-

essing, such as image thresholding and objects filtering values.

During observation of roundabouts and squares with divers streams of cars and

pedestrians, where bus stops are present together with traffic lights, recognition and

filtering of the various objects and determination of their behaviour is very difficult.

If the road is curved and the system checks for violations as prohibited overtaking or

driving in prohibited lane, the vehicle trajectory will be an arc and not straight line,

which complicates its analysis.

7 Hardware Methods for Improvements

In creating a universal tool for traffic monitoring for all tasks and conditions, with

regard to the accuracy and reliability of the final results, it is appropriate to overcome

the problems at each stage. The optimization process has to start with choosing the

camera and its location, to continue with setting camera parameters and to close with

video footage processing and filtrating algorithms and parameters.

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7.1 Mounting Place and Video Camera Parameters

The cameras are mounted on a certain height above or on the side off the road. They

have to be pointed down towards the road, and all observed lanes have to leave the

frame at the top and bottom, not sideways. The sky must not enter the frame in order

to reduce ultraviolet light, brightness changes, camera dazzle by headlights, etc. Sur-

rounding objects and light sources, which may hinder processing algorithms, must

also be minimized.

Stationary cameras without zoom capability and progressive frame scanning are

used. Interlaced scan cameras require further signal processing before information

from the signal can be extracted.

To avoid having to adjust the iris, lenses with shorter focal length have to be used

to keep the depth of field long enough during the night, when the iris opens to allow

more light. A wide-angle lens provides a larger viewing area, but it introduces also

heavier geometric distortions.

When expected that the substructure will experience vibrations, it is necessary to

use a camera with image stabilization. It is sufficient to compensate the linear dis-

placements of the image sensor, because there is usually no rotation. Stabilization can

be achieved in two ways – mechanical, by movable lens and digital, by image proc-

essing.

Useful video functions are AGC (Automatic Gain Control) and AES (Automatic

Electronic Shutter). AGC function adjusts the gain of analogue signal from image

sensor at different levels of illumination. This function is useful during the evenings

and night. AES function is responsible for the shutter speed, which affects the bright-

ness of the frame. For many algorithms is important that the difference between con-

secutive frames is minimal. To achieve this, the two functions should work, so that

the parameters of the frame remain constant, even when the amount of sunlight

changes. Rapid change in brightness of the image is observed when a large vehicle

passes near the camera. Such a vehicle occupies big part of the frame and the large

surface reflects more light toward the camera.

7.2 Additional Optical Filters

The overall quality of the video frames can be improved by adding optical filters in

front of the camera lens. Filters for contrast enhancement are suitable for places with

frequent fog or haze. Polarizing filters reduce the glare from water, wet and metal

surfaces such as the coverage of most vehicles. Ultraviolet and infrared light filters

improve colour reproduction and durability of the image sensor in the video camera.

7.3 Processing Units

The performance of microprocessors is growing steadily and today it is sufficient for

the purpose of digital image processing in real time and in particular for video traffic

monitoring. There are different technologies for the construction of computer mod-

ules for general and specialized applications, such as:

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− digital signal processors (DSP),

− system on a chip (SoC),

− field-programmable gate arrays (FPGA),

− application-specific integrated circuit (ASIC).

Some of the solutions use hard-coded algorithms that can not be changed once the

chips are manufactured or once software is written to the chip. Since all functions of

video traffic sensors are realized by a software program it is desirable to be able to

change and enhance them after initial system deployment. In this sense it is better to

use standard solutions such as DSP processors, enabling easy software update.

When using a dual-core DSP processor, each core can process an individual frame,

so the cores will have twice the time to finish the processing. Another option for

optimization is to separate the series of processing operations between the two cores.

So the volume of tasks for each core will be smaller and will require less time.

8 Software Methods for Improvements

Having selected the optimal location and camera, here comes the optimization of the

filtering and processing algorithms for video signal analysis. Filtering is a basic

method for removing erroneous values in the data, which increases the accuracy of

the final result.

8.1 Camera and Traffic Parameters

In visual identification of vehicles, the ability to set different system parameters is an

advisable step. The configuration of the observed road section is static so it is possi-

ble to preset parameters describing the road, camera characteristics and normal ranges

of traffic parameters. Some systems rely on the relationship between the size of the

frame in pixels and the actual size of the observed road. It is even possible by setting

the actual coordinates of several points of the frame to determine three-dimensional

location and type of vehicles, without using stereoscopic camera [6]. Such configura-

tion parameters can be:

− road length,

− number and lane boundaries,

− driving direction for each lane,

− real distance between virtual sensors,

− mounting height of the video camera,

− viewing angle or focal length of the lens,

− central point of the intersection,

− zones not subject to analysis.

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8.2 Processing and Filtering Parameters

Noise filtering should be done in two stages. The first stage covers video frame proc-

essing before measuring vehicle parameters and the second involves the evaluation of

the estimated parameters.

The first stage could include reducing colour noise, contrast adjusting, colour nor-

malization, application of smoothing filters, frame thresholding or difference between

two consecutive frames and more. Reducing noise in the image is useful in cases

where optical flow is calculated. Normalization of frames is aimed at eliminating

unevenness in brightness. The application of morphological operation "erosion" on

the binary image removes small objects occurring due to noise, rain, movement of

trees, birds, animals, etc. In order to eliminate false objects or those which are not

important, an area of interest must be set and only in this area objects are analyzed.

This is useful when is not possible to avoid areas close to the road with common

movements like sidewalks and parking spaces.

It is possible that even after frame filtering and separation of potential vehicles, the

extracted data contains objects that are not cars or with incorrect parameters due to

some reason. Such objects are moving with unreal speed, suddenly changing direc-

tion, got too large or too small. Characteristics of the vehicles slightly change be-

tween successive frames and the big difference have to be considered as not normal

and corresponding object filtered out. Example of a jump-like change of the parame-

ters of a vehicle is where two or more vehicles overlap visually or by their shadows.

Additional filtering parameters can be the valid ranges for vehicle size. For example,

motorcyclists and cyclists occupy significantly less space than cars, and trucks and

buses more space. This space can be measured during on-site trial tests and later setup

in the software. Only after this stage the system can proceed by reporting the real

count of vehicles, calculation of additional traffic indicators and checking for viola-

tions.

8.3 Vehicle Detecting Algorithms

In the theory of digital image processing, there are numerous operations. Different

analyzing algorithms apply different sets of operations for a given task and therefore

their efficiency and accuracy is varying and depends on many factors. The main task

of these algorithms is the detection of moving and stopped vehicles on the road. Other

task may include speed and vehicle type recognition. Development of fast and reli-

able algorithm is a process requiring time and deep knowledge. Different weather and

road conditions may require the use of several methods to achieve accurate data in a

wide range of situations.

At present there are numerous methods for detecting objects in video footage.

They can be classified into 3 groups:

− methods using prior information about objects,

− methods based on movement or change in video footage,

− methods based on wave analysis.

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Preliminary information used to identify an object as a vehicle can be symmetry,

colour, contours of the vehicle, the presence of round objects (wheels), 3D models

and headlights. Symmetry in the horizontal and vertical directions can be used to

distinguish vehicles from other objects. Due to the typical colour of asphalt and its

uniformity, opening the car can be based on colour differences. The presence of a

dark shadow is a sure sign of the presence of a car. This method is limited to places

with no side objects near the road.

On the basis of uniform colour and the lack of contours in the image and during

vehicle passage contours appear, which can be used to classify vehicle by type by

creating its wire 3D model. Change in a certain way of intensity of the image, also

could be signal for the presence of a vehicle.

Classification of vehicles according to the distance between the front and rear axle

(wheels) requires the camera to be positioned laterally and preferably perpendicular

to the road. Another method used during the night time is the detection of a pair of

punctuated bright objects representing vehicle’s headlights.

All methods discussed so far use spatial features to separate vehicles from the

background. Another used method is the calculation of optical flow. The vector field

of the moving object is called optical flow. Here several frames are used, i.e. the

temporal characteristics of the objects. The calculation of optical flow is a heavy

computational task that is only applicable to moving objects. This prevents the

method from applying on congested roads and intersections, because stopped vehicles

can’t be detected.

Wave transformation is a new method for image processing. Movement of vehicles

is described in full 3D spatial-temporal model covering several video frames.

8.4 Computation Difficulty and CPU Power

Another area influencing the final results and functionality of the system is the com-

putational difficulty of the algorithms. Since it is important to obtain data in real time,

it is necessary to pay attention to the following factors:

− volume of the video signal,

− the efficiency of the processing algorithms,

− the performance of microprocessors.

Volume of the input signal W is defined as a product of width and height of the frame

in pixels multiplied by the number of frames per second and is measured in pixels/s:

W = F

. (1)

Where W - volume, F

- width of frames, F

- height of frames, F

- frames per

second. The bigger is the frame and higher the frame rate is, the larger the volume of

the input signal per second is, which requires a faster processor.

The implementation of additional filtration of the frames or more precise algo-

rithms requires the calculations to be optimized. Common approach for speeding up

calculations is to use integer arithmetic, since this type of operations are faster com-

pared to floating point operations. Another optimization can be reducing the volume

of the input signal via a pre-resizing of the frames by a given ratio smaller than 1.

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Frame rate of 10 fps is sufficient to obtain accurate data, but typically the standard

TV speed of 25 or 30 fps is used to obtain smooth video.

9 Conclusions

The video sensors for traffic monitoring are well-suited for a wide range of transport

tasks because in addition to measuring individual vehicle parameters, they can also

detect traffic violations, queues of cars and unwanted situations such as traffic jams.

They provide coverage of a large number of lanes on a long road section, which

makes them attractive. The lack of necessity for stopping the traffic during installa-

tion and maintenance activities, as well as the possibility for using already installed

video cameras, makes this solution even more valuable for transport authorities. The

opportunity to expand their software functions remotely from the control centre,

without changing the cameras, makes from video traffic sensors a promising technol-

ogy with long life.

The guidelines for future development in the sphere of video traffic monitoring can

be in the direction of improving the techniques and algorithms for vehicle detection in

the cases of merging several nearby vehicles and working during night time. There

are video sensors using stereoscopic cameras addressing the problem with merging of

adjacent vehicles. These video sensors determine exact spatial location of vehicles on

the road and their type.

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