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This paper presents an intelligent traffic light control method based on extension Convolution neural network (CNN) theory for crossroads. First, the number of passing vehicles and passing time of one vehicle within green light time period are measured in the main-line and sub-line of a selected crossroad. Then, the measured data are adopted to construct an estimation method based on CNN for recognizing the traffic flow of a standard crossroad. Some experimental results are made to verify the effectiveness of the proposed intelligent traffic flow control method. The diagnostic results indicate that the proposed estimated method can discriminate the traffic flow of a standard crossroad rapidly and accurately.
In modern life we have to face with many problems one of which is traffic congestion becoming more serious day after day. Traffic flow determination can play a principle role in gathering information about them. This data is used to establish censorious flow time periods such as the effect of large vehicle, specific part on vehicular traffic flow and providing a factual record of traffic volume trends. This recorded information also useful for process the better traffic in terms of periodic time of traffic lights. There are many routes to count the number of vehicles passed in a particular time, and can give judgment of traffic flow. Now a day?s camera-based systems are better choices for tracing the vehicles data. This project focuses on a firmware-based novel technique for vehicle detection. This approach detects the vehicles in the source image, and applies an existing identifier for each of the vehicle. Later it classifies each vehicle on its vehicle-type group and counts them all by individually. The developed approach was implemented in a firmware platform which results is better accuracy, high reliability and less errors. Traffic lights play a very significant role in traffic control and regulation on a daily basis. Using OPENCV ?the density of the roads is determined and the microcontroller changes the duration of green light given for each road as per the output after image processing.
The traffic lights used in India are basically pre-timed wherein the time of each lane to have a green signal is fixed. In a four lane traffic signal one lane is given a green signal at a time. Thus, the traffic light allows the vehicles of all lanes to pass in a sequence. So, the traffic can advance in either straight direction or turn by 90 degrees as shown in Fig.1. So even if the traffic density in a particular lane is the least, it has to wait unnecessarily for a long time and when it gets the green signal it unnecessarily makes other lanes wait for even longer durations
In this system we are going to implement crowd based traffic control? light system using CNN ,lane will be get open on the basis of crowd at the desired lane.It is identified by the capturing the vehicle crowd images in the lane and identifying the number of vehicles with redcolor in that desired lane
Pre-processing?is a common name for operations with?images?at the lowest level of abstraction — both input and output are intensity?images.o The aim of pre-processing?is an improvement of the?image?data that suppresses unwanted distortions or enhances some image?features important for further processing.
Background subtraction, also known as?foreground detection, is a technique in the fields of?image processing?and?computer vision?wherein an image’s foreground is extracted for further processing (object recognition etc.). Background subtraction is a widely used approach for detecting moving objects in videos from static cameras. The rationale in the approach is that of detecting the moving objects from the difference between the current frame and a reference frame, often called “background image”, or “background model”. Background subtraction is mostly done if the image in question is a part of a video stream. Background subtraction provides important cues for numerous applications in computer vision, for example surveillance tracking or human poses estimation.
 Vikramaditya Dangi, Amol Parab, “Image Processing Based Intelligent Traffic Controller”, Undergraduate Academic Research Journal (UARJ), ISSN : 2278 ? 1129, Volume-1, Issue-1, 2012.  Raoul de Charette and Fawzi Nashashibi, ?Traffic light recognition using Image processing Compared to Learning Processes?.
 Mriganka Panjwani, Nikhil Tyagi, Ms. D. Shalini, Prof. K Venkata Lakshmi Narayana, ?Smart Traffic Control Using Image Processing?.
 Shiu Kumar”UBIQUITOUS SMART HOME SYSTEM USING ANDROID APPLICATION” International Journal of Computer Networks & Communications (IJCNC) Vol.6, No.1, January 2014
 M.Fathy,M.Y.Siyal,?An image detection technique based on morphological edge detection and background differencing for real time traffic analysis,? pattern recognition letters,vol-16,pp.1321-1330,1995.