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T RAFFIC JAM DETECTION SYSTEM
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.
5
T RAFFIC JAM DETECTION SYSTEM
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. Computers can recognize only binary signals and a picture of the road can be represented as a digital image. 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. 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
5
T RAFFIC JAM DETECTION SYSTEM
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 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.
5
T RAFFIC JAM DETECTION SYSTEM
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 ‘Erosion-Blob Technique of Object Extraction’.
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 ‘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.
1.3.5 Result Representation We simulate our system with an application developed in the .Net Framework.
5
T RAFFIC JAM DETECTION SYSTEM
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.
TRAFFIC JAM DETECTION SYSTEM
Chapter 2 Background
5
T RAFFIC JAM DETECTION SYSTEM
TRAFFIC JAM DETECTION SYSTEM
Chapter 3 Work Approach
5
T RAFFIC JAM DETECTION SYSTEM
5
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.
5
T RAFFIC JAM DETECTION SYSTEM
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. Computers can recognize only binary signals and a picture of the road can be represented as a digital image. 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. 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
5
T RAFFIC JAM DETECTION SYSTEM
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 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.
5
T RAFFIC JAM DETECTION SYSTEM
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 ‘Erosion-Blob Technique of Object Extraction’.
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 ‘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.
1.3.5 Result Representation We simulate our system with an application developed in the .Net Framework.
5
T RAFFIC JAM DETECTION SYSTEM
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.
TRAFFIC JAM DETECTION SYSTEM
Chapter 2 Background
5
T RAFFIC JAM DETECTION SYSTEM
TRAFFIC JAM DETECTION SYSTEM
Chapter 3 Work Approach
5
T RAFFIC JAM DETECTION SYSTEM
5
