Traffic Jam Detection System by Ratul, Sadh, Shams

[Type the document title] [Year]

Khulna University of Engineering & Technology
Department of Computer Science and Engineering

CSE 3100
Report on Software Development Project – II

Traffic Jam
Detection System

Date:
22 June, 2008

Authors:
Khan Muhammad Nafee Mostafa
0507007
&
Qudrat-E-Alahy Ratul
0507037

Supervisor:
Rushdi Shams
Lecturer,
Dept of Computer Science and Engineering,
Khulna University of Engineering & Technology,
Bangladesh

Acknowledgement We express our gratitude to
the Almighty and our family.

We also thank Batch 2k5 of
CSE and whole CSE
department of KUET for
being a beaming source of
inspiration.

Thousands of people have
worked and been working in
the field of Image
Processing and Object
Recognition. Our respects go
to all of them.

We dedicate our work to
them who relate to traffic.

TRAFFIC JAM DETECTION SYSTEM


Contents

Chapter 1: Introduction…01
1.1 Traffic Jam Detection…01
1.2 Techniques of Traffic Jam Detection…02
1.3 A Brief of Our Approach…03
1.4 Outcome of the Project…04

Chapter 2: Background…05
2.1 About Traffic Jam…05
2.2 About Image Processing and Object Detection…06
2.3 Fundamental Steps in Digital Image processing…06
2.4 Idea of Image…07
2.5 Towards Traffic Jam Detection…07

Chapter 3: Work Approach …08
3.1 Approach of Image Processing and Object Detection…08
3.2 Image Acquisition…08
3.3 Image Enhancement…09
3.4 Lane Masking…10
3.5 Further Enhancement…11
3.6 Erosion Operation…12
3.7 Adaptive smoothing…13
3.8 Blobs (Binary Large Objects)…14
3.9 Object Analysis…14
3.10 Motion Detect…15
3.11 Showing Result…15

Chapter 4: Result and Calculation…17

Chapter 5: Conclusion…18

Appendix I : Screenshot of Our  Application… A‐1


Chapter
1
Introduction

Traffic system is at the heart of civilized world and development in many
aspects of life relies on it. Excessive number of traffic in roads and improper
controls of that traffic create traffic jam. It either hampers or stagnate
schedule, regimen, business, and commerce. Automated traffic detection
system is therefore required to run the civilization smooth and safe‐ which
will eventually lead us towards proper analysis on traffics, proper adjustment
of control management, and distribution of controlling signals.

The aim of this project is to develop a traffic jam detection system using
image processing and object detection. Besides, we developed an application
that uses this detection mechanism and provides instant online information
through Short Messaging Service (SMS) and e‐mail. This information helps
the travelers to incorporate with current situation on specific roads and the
authority to plan and schedule load balancing on the roads.

1.1 Traffic Jam Detection

Mostly, automated systems need the interaction with a computer that
requires an algorithm to meet the specific requirements.

Human eye can easily detect whether there is a traffic jam or not. Within less
than a second, human brain processes the image of the traffic, detects and
analyzes objects, and thereafter comes to a decision. A programmer also
detects, as being a human, traffic jam according to the analysis and detection
of objects. But implementation of such a biological process requires some
special treatment.

1


Computers can recognize only binary signals. A picture of the road can be
represented as a digital image, which is actually binary signal. This image is
used as primary source of data. But, an image, when it is captured from
natural environment, is very raw and unformatted. Programmers have to
process the data and extract relevant information from images. Frequent
need of extracting information from images has led to the development of
several fields (e.g. Image processing, Computer Vision, Object recognition
etc) in computing industry. We have used image processing and object
detection to detect traffic jam. It takes several steps of image processing for
us to make decision about traffic jam. Key points of these steps are,

• Image Analysis
• Object detection
• Typed object count
• Motion detection
• Result representation

We have integrated these steps in our system and have developed an
application to simulate it.

1.2 Techniques of Traffic Jam Detection

Several approaches have been taken to detect traffic jam. Oldest and most
reliable approach of them is to employ a person in important traffic points.
But with the advent of technology and increment of traffic flow, several
approaches with less involvement of human have been taken.

Magnetic Loop Detector (MLD) used to count the number of plying vehicles
using magnetic properties [Koller et al. 1994].

Light beams (IR, LASER etc) are also use. As traffic move on road light beams
are cut. Electronic devices can record these events and detect traffic jam.

In contemporary approaches, image processing, computer vision etc are
highly recommended. In these types of approaches involvement of
computers provide online characteristics, facilitate centralized control over
distributed system and develop compact platform. Computer vision (or Robot
Vision) can also provide other services. In these approaches, information feed

2


through telephone or web networks can easily be supported. Even, traffic
flow of whole city can be observed from a centre and statistics can be made.
Traffic systems design and urban planning can be very efficient by taking
statistics from computer aided traffic systems.

We are using computer aided image processing to attain optimal support.

1.3 A Brief of Our Approach

In our project, as mentioned earlier, we have used image processing. Major
steps of our work are presented next,

1.3.1 Image Analysis
At first we must capture the image of the traffic point where we are going to
detect traffic jam. This image is taken using a good digital camera. Usually
some fundamental image enhancements using Gaussian filter and noise
reductions are done by camera itself. So, we do not need to apply them again
and kept this step for camera.

1.3.2 Object Detection
This step is very crucial. We used ‘Lane Masking’ [Atkociunas et al. 2005]. After
masking lane, the image contains only traffic vehicles. Then ‘Euclidean Color
Filter’ is applied. It will fill pixels with white where the difference of color is
minimal. Then, the image will be converted into a binary (first ‘Grayscale’ and
then ‘Black & White’) image using a threshold value. Afterwards, ‘Erosion’
operator and ‘Adaptive Smoothing’ are applied. Finally, ‘blobs’ are found
from the image and are placed apart. We recognize these blobs as objects.

We will call this technique as ‘Lane Masked Erosion‐Blob Technique of Object
Extraction’.

3


1.3.3 Typed Object Count
Object detection is followed by object count. We, classify all the objects
found, in previous step, into four groups (small, large, critical and perfect)
and count them. This information is provided to determine the present state
of traffic.

We will call this technique as ‘Lane Masked Erosion‐Blob Technique of Typed
Object Count’.

1.3.4 Motion Detection
To detect motion, we need two consecutive captures of the road. Both
images undergo ‘Erosion‐Blob Technique of Object Extraction’ (section 1.3.2).
Then, we detect overlapped blobs. Then, we calculate the distance the same
blob has moved. Finally we average the result to determine speed of plying
traffic.

We will call this technique as ‘Lane Masked Erosion‐Blob‐Displacement
method of motion detection’.

1.3.5 Result Representation
We simulate our system with an application developed in the .Net
Framework.

1.4 Outcome of the Project

Our project is targeted at developing a tool for detecting traffic jam. This
project reaches the target successfully and can be implemented in various
online systems. We can feed information about:

• Number of plying vehicle
• Existence of Jam
• Average speed of traffic

In the next chapters we will represent details of our project.

4


Chapter
2
Background

2.1 About Traffic Jam

It is said that road and transport system is the mean by which we can
measure the development level of a country. And Traffic jam is the
unavoidable part of road & transport. Insufficient roads, poor urban planning
etc may cause traffic jam. In general sense, we say that there is a traffic jam
when there plies more vehicles in a road than it can hold.

In any one of the following cases, we say there is a traffic jam:

• The vehicles are stopped

• Vehicles are moving very slowly

• There is an excessive number of vehicle on road

Automated traffic control system can help us to reduce traffic jam and save
our valuable time. Again, computer aided traffic jam detection system can
warn people about traffic points with traffic jam and ask people to try
alternative path. We worked on traffic jam detection to build a base of such
modern facilities to ensure better transport.

5


2.2 About Image Processing and Object Detection

Recently computing industry is working a lot in both image processing and
objects detection. These two fields are contributed in the development of
artificial intelligence and automated systems.

According to Nils J. Nilson [Nilson, 2002], Image processing has been proved
to be a very difficult problem for machines. Programmers mainly face
difficulties from

• Variable and uncontrolled illumination
• Complex and hard to describe objects
• Objects occluding other objects
In man made environment difficulties are lessened than in natural
environment.

Object detection is the outcome of image processing. Object recognition goes
far more deep in analyzing images and objects from it.

2.3 Fundamental Steps in Digital Image processing

Some fundamental steps of image processing are,

• Image Acquisition
• Image Enhancement
• Image Restoration
• Color Image Processing
• Wavelets processing
• Compression
• Morphological Processing
• Segmentation
• Representation and Description
• Recognition

(Please look for references, [Gonzalez & Woods, 2003], [TUDelft])

6


2.4 Idea of Image
In most contexts, we studied and worked on, the word ‘image’ is used to
mention a two dimensional array (or matrix or a plane) of bitmaps and is
used to represent 2D vision of real‐life images.

Every bitmap contains 3 fields: R, G and B. Respectively, they contain the
color values of red, green and blue of a particular point (pixel). Sometimes,
another fourth field may contain intensity of the very point.

2.5 Towards Traffic Jam Detection
Computer industry is getting enriched in image processing everyday. A lot of
work of many individuals throughout the world has helped us to develop a
traffic jam detection system.

7


Chapter
3
Work Approach

We have developed this project as a part of a course named ‘Software
Development Project ‐ II’ within the undergraduate engineering program in
Computer Science and Engineering. We were suggested to use the .Net
framework as platform and C# as the language. To accomplish our project C#
.Net was a good tool. Amongst several different approaches of image
processing we have selected some to use. A precise description of our work is
presented in this chapter.

3.1 Approach of Image Processing and Object Detection

A machine, just like a human being, requires to ‘see’ the road for detecting
traffic jam on it. To provide a machine the capability to see we must exactly
follow the process a human being does.

Image processing requires huge processing of data and several algorithms run
in order of n2 or ny. Therefore, processes involving image processing are to be
handled with care.

3.2 Image Acquisition

Image acquisition is the first step of our system. A precise image of the case
road (traffic point) is acquired. We need to capture an image of the road with
no vehicle plying on it. This is called as background image (fig 3.2‐b).

8


Another image of the road is taken exactly at the time when we need to
detect traffic jam. We have a sample image next (fig 3.2‐a).

Fig3.2‐a: Sample Image taken on road at runtime

Fig3.2‐b: Sample Image taken as the background image

3.3 Image Enhancement

Some noise frequently comes on images taken on natural environment.
Contemporary technology brings cameras integrated with image enhancing
algorithms. So, we are not applying any enhancement separately.

9


3.4 Lane Masking
This method is intended to separate the part of the road where vehicles are
moving in one direction (Fig. 3.2‐a). This method simplifies the processing. As
we eliminate background we have only to think about the objects. So, a huge
amount of irrelevant information is discarded.

Masking algorithm is given by formula

N(p) = M(p) × V (p),

Where,
M(p) is an image point value in primary frame,
N(p) is a new image point in the output image (Fig. 3.4‐a),
V(p) is mask value for point p: V (p) = 0 if corresponding pixel is eliminated,
otherwise V (p) = 1.


Masking is applied to each RGB color separately.


Fig3.4‐a: Image after lane masked from main image

‐ =
Fig 3.4‐b: the Lane Masking Process

10


3.5 Further Enhancement
After lanes are masked, the images are then enhanced by turning it into
grayscale (fig3.5‐a).

Grayscale algorithm is

R=G=B= (r+g+b)/3

Where,
R, G and B are new values of red, green and blue
r, g and b are old values of red, green and blue

Then the image is converted into black & white binary image (fig3.5‐b). This
requires the algorithm,

If (r+g+b > threshold) {
R=G=B = white;
}
Else {
R=G=B=black;
}

Fig3.5‐a : Grayscal conversion

Fig 3.5‐b : Binary Converted Image

11


3.6 Erosion Operation
The Erosion operator is defined as,

Fig 3.6‐a: Erosion Operator

In the case of object extract we can get good result by applying this operator.
Fig 3.6‐b depicts an image with erosion applied on it.

Fig3.6‐a: Figure with erosion applied on it

12


3.7 Adaptive smoothing
While traditional smoothing methods use the same window shape and size to
smooth an image independent of its content, in adaptive smoothing, the
window shape and size are varied across the image domain depending on
local image content. In adaptive smoothing a window is sized according to
the gradient magnitude locally and shaped in such a way that it has a shorter
side across an edge compared to along the edge. This mechanism maintains
edge details while smoothing random noise.

Fig3.7a shows the picture processed with adaptive smoothing. And fig3.7b is
without adaptive smoothing.

Fig 3.7a: Figure after Adaptive smoothing

Fig 3.7b: Figure without Adaptive smoothing

13


3.8 Blobs (Binary Large Objects)
Blobs are the largest objects. To find blob, we blur the image to eliminate all
small noises. Figure below shows the largest blob found by our method, ‘Lane
Masked Erosion‐Blob Technique of Object Extraction’.

Fig3.8a: Perfect Blob of 7 found blobs

3.9 Object Analysis

Blobs found in previous step is now processed mathematically to classify
them in 4 groups,

• Small
• Critical
• Perfect
• Large

Small objects are discarded as they are mostly noises. Large objects are never
car or bus. They can be the image itself or other large portion of image. Most
of the cars lie in the perfect blob group. But sometimes critical blob hold
portions of car (hood, trunk etc).

We count the number of blobs in each class. Then the count is compared to
standard conditions and a decision about jam status is then can be made.
This approach can be called as ‘Lane Masked Erosion‐Blob Technique of
Typed Object Count’.

14


3.10 Motion Detect

Motion detection is another good way of telecasting information about traffic
jam. To do so,

• we take two  consecutive images of the road
• find the blobs
• detect overlapping blobs
• find axis‐wise displacement
• calculate the average displacement to determine speed

This approach can be called as ‘Lane Masked Erosion‐Blob‐Displacement
method of motion detection’.

Two images are given below showing motion detection:


Fig3.10a: two images for detecting traffic motion

3.11 Showing Result

In all the stages, described above, necessary information for deciding
whether there is a traffic jam or not is produced. Now we can display or even
feed this information to several systems.

In addition to traffic jam detection our simulation program can,

• Generate string describing traffic status
• Receive request through  SMS
• Respond to a SMS immediately
• Receive request through e‐mail
• Respond to e‐mail requests

15


Total integration of our application has also included set‐up features and set‐
up data are stored with the help of filing system.

16



Chapter
4
Result and Calculation

Our project has been tested in several conditions. We have found that, it
takes 3 to 4 second for our system to determine the objects and their speed
when the image is 1024*768 pixels.

Our system can work efficiently work in standard environment.

17


Chapter
5
Conclusion

Traffic jam recognition systems have been developed in several different
ways. Our approach is analytical. It also takes the advantage of statistical
data. Lane masking has contributed greatly in reducing time complexity and
has tremendously have improved performance.

In future our work can support online centralized traffic system for a city. In
this system, every important traffic point will be monitored with our ‘Traffic
Jam Detection System’. Then, information will be fed to central server. This
server can analyze all points’ traffic state and can control traffic flow. Such
system may reduce traffic jam greatly.

18

References
[Atkociunas et al. 2005] http://www.lana.lt/journal/19/Atkociunas.pdf

[Gonzalez & Woods, 2003] Rafael C. Gonzalez, Richard E. Woods,”Digital Image
Processing ”2/e,2003

[Koller et al. 1994] Dieter Koller, Joseph Weber and Jitendra Malik,
"Robust Multiple Car Tracking with Occlusion Reasoning" ECCV (1)
1994

[Nilson, 2002] Nils J. Nilson “Artificail Intelligence”, 2002

[TUDelft] http://www.qi.tnw.tudelft.nl/Courses/FIP/noframes/fip.html

Appendix I: Screenshots of Aour Application

A‐1

Traffic Jam Detection System by Ratul, Sadh, Shams

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