Automatic traffic monitoring and surveillance are important for road usage and congestion control. For Intelligent Transportation systems (ITS), various sensors have been employed to estimate traffic parameters for updating traffic information. Magnetic loop detectors have been the most used technologies, but their installation and maintenance are inconvenient and might become incompatible with future ITS infrastructure. Thus, there is a need to improve existing traffic light system in order to manage the traffic flow in smooth and efficient way. It is well recognized that vision-based camera system are more versatile for traffic parameter estimation. In addition to qualitative description of road congestion, image measurement can provide quantitative description of traffic status including speeds, vehicle counts, etc. Moreover, quantitative traffic parameters can give us complete traffic flow information, which fulfills the requirement of traffic management theory. Image tracking of moving vehicles can give us quantitative description of traffic flow.
The proposed system will be able to do the following operations efficiently:
1. To differentiate whether the vehicle is present or absent.
2. Adjustment of the time period of timer according to traffic density.
3. Signal the traffic light to go red or green according to the presence of traffic on the road.
Components of the current project
1. Hardware module
• Image sensors- to get the images.
• Computer- to process the images.
• Platform or Model- prototype of the real world traffic light control system.
2. Software module
• MATLAB version 7.8 as image processing software
3. Interfacing
• The interfacing between the hardware prototype and software module is done using parallel port of the personal computer. Parallel port driver has been installed in the PC for this purpose.
RESULT AND DISCUSSION
On performing the experiments, depending on the traffic intensity on the road, we get the following results regarding on time durations of various traffic lights.
1. If the reference image and the new image matches 10 to 50 % then traffic light will give green signal for 60 seconds.(fig. 1)
2. If the reference image and the new image matches 50 to 70 % then traffic light will give green signal for 30 seconds.
3. If the reference image and the new image matches 70 to 90 % then traffic light will give green signal for 20 seconds.
4. If the reference image and the new image matches 70 to 90 % then traffic light will give green signal for 10 seconds.
Figure 1: image matches 10 -50%
EXPERIMENTAL:
Initially image acquisition is done with the help of web camera
• First image of the road is captured, when there is no traffic on the road
• This empty road’s image is saved as reference image at a particular location specified in the program.
Now the image is sent for enhancement. The acquired image in RGB is first converted into gray. Now we want to bring our image in contrast to background so that a proper threshold level may be selected while binary conversion is carried out. This calls for image enhancement techniques. The objective of enhancement is to process an image so that result is more suitable than the original image for the specific application. For this power law transformation has been used. The power law transformations have the basic form
s = crγ
Where S is output gray level, r is input gray level, c and γ are positive constants.
The enhanced image is sent for Edge Detection.
Step 1: Edge detection: Among the key features of an image i.e. edges, lines, and points, we have used edge in our present work which can be detected from the abrupt change in the gray level. An edge essentially demarcates between two distinctly different regions, which means that an edge is the border between two different regions.
Here we are using edge detection method for image matching:
• Edge detection methods locate the pixels in the image that correspond to the edges of the objects seen in the image.
• The result is a binary image with the detected edge pixels.
We have used gradient based Edge Detection that detects the edges by looking for the maximum and minimum in the first derivative of the image.
• First derivative is used to detect the presence of an edge at a point in an image.
• Sign of the second derivative is used to determine whether an edge pixel lies on the dark or light side of an edge.
The change in intensity level is measured by the gradient of the image. Since an image f (z, y) is a two dimensional function, its gradient is a vector
