Traffic Jam Detection System by Ratul, Sadh, Shams

[Type the document title] [Year]

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

Khulna University of Engineering & Technology

Department of Computer Science and Engineering

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


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TRAFFIC JAM DETECTION SYSTEM 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.


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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


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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’.


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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.


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TRAFFIC JAM DETECTION SYSTEM 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.


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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])


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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.


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TRAFFIC JAM DETECTION SYSTEM 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).


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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.


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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


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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


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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


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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


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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’.


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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


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


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TRAFFIC JAM DETECTION SYSTEM 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.


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TRAFFIC JAM DETECTION SYSTEM 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.

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

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Appendix I: Screenshots of Aour Application