5 Mins = half hour

10 Mins = 45 Mins

15 Mins = 1 and a half hour.

?

AUTONOMOUS FOUR

PHASE INTERSECTION

TRAFFIC LIGHTS WITH

MINIMUM DELAY

Motivation

As Population is expanding day by day, so are the vehicles on the road. The road congestion is

becoming a considerable problem especially in India. How much early I get up, I always miss

my first class at college or sometimes I had reached at lunch break too. I see hundreds of

vehicle standing and just wasting fuel because of the unoperated traffic lights. Is this the way

of sustainable development ?

Conventional methods are even

better than the today's traffic light

system. So I decided to make the

system dynamic and more superior

to eradicate the Delay waiting

period at the intersections.

Introduction

Dynamic Timings of phase : Calculation of Traffic density of four phase intersection using image processing which is done on videos of vehicles that are captured using digital camera. We have chosen image processing for calculation of traffic density as cameras are very much cheaper than other devices such as sensors. According to the calculated density, optimum green ratio or we can say that allotment of green light is done in an efficient manner to reduce the delay of vehicles or indirectly the congestion of traffic.

VIDEO CAPTURE : By Four Cameras At Four Sides of the Intersection.

VIDEO PRE-PROCESSING:

• RGB TO GRAY COLOUR CONVERSION

• GAUSSIAN BLUR

• MORPHOLOGICAL OPERATIONS

• BACKGROUND SUBTRACTION

• BLOB TRACKING

VEHICLE COUNTING OF FOREGROUND MASK IMAGES

ALLOTMENT OF TIME USING WEBSTERS EQUATION

Image Processing

Image processing: Image processing is the process of manipulating image data in order to make it suitable for computer vision applications or to make it suitable to present it to humans. For example, changing brightness or contrast is a image processing task which make the image visually pleasing for humans or suitable for further processing for a certain computer vision application.

Image acquisition

Image manipulation

Obtaining relevant information

Decision making

Image Processing with opencv/c++: OpenCV is an open source C++ library for image processing and computer vision, originally developed by Intel and now supported by Willow Garage.

It is a library of many inbuilt functions mainly aimed at real time image processing. Now it has several hundreds of image processing and computer vision algorithms which make developing advanced computer vision applications easy and efficient.

BLOCK DIAGRAM AND VIDEO

Image Pre-Processing

RGB TO GRAY COLOUR CONVERSION

Signal to noise : For many applications of image processing, color information doesn't help us identify important edges or other features. There are exceptions. If there is an edge (a step change in pixel value) in hue that is hard to detect in a grayscale image, or if we need to identify objects of known hue (orange fruit in front of green leaves), then color information could be useful. If we don't need color, then we can consider it noise. At first it's a bit counterintuitive to "think" in grayscale, but you get used to it.

Complexity of the code : If you want to find edges based on luminance AND chrominance, you've got more work ahead of you. That additional work (and additional debugging, additional pain in supporting the software, etc.) is hard to justify if the additional color information isn't helpful for applications of interest.

Color is complex : Humans perceive color and identify color with deceptive ease. If you get into the business of attempting to distinguish colors from one another, then you'll either want to (a) follow tradition and control the lighting, camera color calibration, and other factors to ensure the best results, or (b) settle down for a career-long journey into a topic that gets deeper the more you look at it, or (c) wish you could be back working with grayscale because at least then the problems seem solvable.

MORPHOLOGICAL TRANSFORMATION

IN IMAGE PROCESSING

Morphological transformations are some simple operations based on the image shape. It is

normally performed on binary images. It needs two inputs, one is our original image, second

one is called structuring element or kernel which decides the nature of operation. Two basic

morphological operators are Erosion and Dilation. Then its variant forms like Opening,

Closing, Gradient etc also comes into play. We will see them one-by-one with help of

following image:

Threshold

Erosion

Dilation

It is just opposite of erosion. Here, a pixel element is '1' if atleast one pixel under

the kernel is '1'. So it increases the white region in the image or size of

foreground object increases. Normally, in cases like noise removal, erosion is

followed by dilation. Because, erosion removes white noises, but it also shrinks

our object. So we dilate it. Since noise is gone, they won't come back, but our

object area increases. It is also useful in joining broken parts of an object.

Dilation

The basic idea of erosion is just like soil erosion only, it erodes away the

boundaries of foreground object (Always try to keep foreground in white). So

what it does? The kernel slides through the image (as in 2D convolution). A

pixel in the original image (either 1 or 0) will be considered 1 only if all the

pixels under the kernel is 1, otherwise it is eroded (made to zero).So what

happends is that, all the pixels near boundary will be discarded depending

upon the size of kernel. So the thickness or size of the foreground object

decreases or simply white region decreases in the image. It is useful for

removing small white noises (as we have seen in colorspace chapter), detach

two connected objects etc.

Erosion

GAUSSIAN BLUR

Smoothing, also called blurring, is a simple and

frequently used image processing operation.

Probably the most useful filter (although not the

fastest). Gaussian filtering is done by convolving each

point in the input array with a Gaussian kernel and

then summing them all to produce the output array.

Assuming that an image is 1D, you can notice that the

pixel located in the middle would have the biggest

weight. The weight of its neighbors decreases as the

spatial distance between them and the center pixel

increases.

Background Subtraction Background subtraction (BS) is a common and widely used technique for

generating a foreground mask (namely, a binary image containing the pixels

belonging to moving objects in the scene) by using static cameras.

As the name suggests, BS calculates the foreground mask performing a

subtraction between the current frame and a background model, containing the

static part of the scene or, more in general, everything that can be considered as

background given the characteristics of the observed scene.

Background modeling consists of two main steps:

• Background Initialization;

• Background Update.

In the first step, an initial model of the background is computed, while in the

second step that model is updated in order to adapt to possible changes in the

scene.

CONTOURS AND BLOB TRACKING

Contours can be explained simply as a curve joining all the

continuous points (along the boundary), having same color

or intensity. The contours are a useful tool for shape analysis

and object detection and recognition.

In OpenCV, finding contours is like finding white object from

black background. So remember, object to be found should

be white and background should be black.

There are two functions: cv2.CHAIN_APPROX_NONE (734

points), cv2.CHAIN_APPROX_SIMPLE(only 4 points)

WEBSTERS EQUATION C0 is the Optimum Cycle Length in Sec

L is Total lost time in Sec

𝑖𝑛𝑌 = Total critical volume/saturation flow

Effective green time per phase =𝑌𝑖 𝑖𝑛 𝑌

gt

2 phase diagram for example.

The Green blocks will become dynamic

and will change according to traffic

density.

CONCLUSION and FUTURE WORK

Drawback of earlier methods

• Wastage of time by lighting green signal even when road is empty.

Image processing with websters equation removes such problem.

Slight difficult to implement in real time because the accuracy of time calculation depends

on relative position of camera.

FUTURE WORK

The focus shall be to implement the controller using DSP as it can avoid heavy investment in

industrial control computer while obtaining improved computational power and optimized

system structure. The hardware implementation would enable the project to be used in real-

time practical conditions. In addition, we propose a system to identify the vehicles as they pass

by, giving preference to emergency vehicles and assisting in surveillance on a large scale.

THANK YOU ;)