Nowadays we come across certain problems and traffic congestion is one of them. It is becoming more serious these days. The main reason for traffic jam is the large number of vehicles. To overcome this problem we have manual controlling method. It requires manpower to control traffic. The traffic police will carry the signboard and whistle to control the traffic. Another method we have is automatic controlling method. It is controlled by timers and electrical sensors. The lights are getting on and off depending on the timer value changes.
The drawback of these methods is that manual controlling needs more manpower and in automatic controlling the time is being wasted by a green light on an empty road.
We propose a system for controlling the traffic light by image processing. The proposed system is implemented in MATLAB with an objective to reduce the traffic based on density. It is a technique to enhance raw images received from cameras.
With the uncontrolled population growth, travelling has turned out to be a really hectic task in today’s world.
This is contributing to the wastage of fuel and time. The main reason behind today’s traffic problem is the techniques that are used for traffic management. In order to with this problem, researchers have proposed many solutions. We have selected image processing based traffic control system. It checks the density of vehicles and gives signals according to time allocation.
Humans perceive colour through wavelength- sensitive cells called cones. here are three different varieties of cones.
Each has a different sensitivity to electromagnetic radiation(light) of different wavelength. One cone is mainly sensitive to green light, one to red light, and one to blue light. By emitting a restricted combination of these three colour (red, green and blue), and hence stimulate the three types of cones at will, we are able to generate almost any detectable colour. This is the reason behind why colour image are often stored as three separate image matrices; one storing the amount of red (R) in each pixel, one the amount of green (G) and one the amount of blue (B). We call such colour images as stored in an RGB format. In grayscale images, however, we do not differential how much we emit of different colours, we emit the same amount in every channel. We will be able to differentiate the total amount of emitted light for each pixel; little light gives dark pixels and much light is perceived as bright pixels. When converting an RGB image to greyscale, we have to consider the RGB values for each pixel and make as output a single value reflecting the brightness of that pixel. One of the approaches is to take the average of the contribution from each channel; (R+B+C)/3. However, since the perceived brightness is often dominated by the green component, different, more ‘human-oriented’, the method is to consider a weighted average, e.g.: 0.3R + 0.59G +0.11B.
Image scaling occurs in all digital photos at some stage whether this be in bayer demosaicing or in photo enlargement. It happens anytime you resize your image from one-pixel grid to another. Image resizing is necessary when you need to increase or decrease the total number of pixels. Even if the same image resize is performed, the result can vary significantly depending on the algorithm.
Images are resized because of number of reasons but one of them is very important in our project. Every camera has its resolution, so when a system is designed for some camera specifications it will not run correctly for any other camera depending on specification similarities. So it is necessary to make the resolution constant for the application and hence perform image resizing.
Image enhancement is the process of adjusting digital images so that the results are more suitable for display or further analysis. For example, we can eliminate noise, which will make it more easier to identify the key characteristics.
In poor contrast images, the adjacent characters merge during binarization. We have to reduce the spread of the characters before applying a threshold to the word image. Hence, we introduce ‘POWER- LAW TRANSFORMATION’ which increases the contrast of the characters and helps in the better segmentation. The basic form of power-law transformation is s=cr^? where r and s are the input and output intensities, respectively; c and ? are positive constants. A variety of devices used for images capture, printing, and display respond according to a power-law. By convention, the exponent in the power-law equation is referred to as gamma correction. Gamma correction is important, if displaying an images accurately on a computer screen is of concern. In our experimentation, ? is varied in the range of 1 to 5. If c is not equal to ‘1’, then the dynamic range of the pixel values will be significantly affected by scalling. Thus, to avoid another stage of rescaling after power-law transformation, we fix the value of c = 1. With ? = 1, if the power-law transformed image is passed through binarization, there will be no changes in the results compared to simple binarization. When ? > 1, there will be a change in the histogram plot, since there is an increase of samples in the bins towards the gray value of zero. Gamma correction is important if displaying an image accurately on computer screen is of concern.
Edge detection is the name for a set mathematical methods which aim at identifying points in a digital image at which the image brightness changes sharply or, more technically, has discontinuities or noise. The points at which image brightness alters sharply are typically organized into a set of curved line segments termed edges.
The same problem of detecting discontinuities in 1D signal is known as step detection and the problem of finding signal discontinuities over time is known as changes detection. Edge detection is a basic tool in image processing, machine vision and computer envisage, particularly in the areas of feature reveal and feature extraction.
The canny Edge Detection is one of the most commonly used images processing tools detecting edges in a very robust manner. It is a multi-step process, which can be implemented on the GPU as a sequence of filters. Canny edge detection techniques is based on the three basic objectives.
The image processing based intelligent traffic light control system provides good traffic control mechanism without any time wastage. It overcomes the limitations of all the old traffic control methods. It does not need extra hardware material to detect the vehicles. Only the use of multiple cameras will help to analyze the density of traffic. The proposed system is more accurate and simple.
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