“ADVANCED calculating number of vehicle we are going to

  “ADVANCED TRAFFIC CONTROL MANAGEMENT SYSTEM” Vimala Parangi, Deepti Dhadge, Poonam Kakade, Prof. Poonam Yewale [email protected]

com,[email protected] Abstract: In thispaper we are going to propose a methodology for determining traffic congestionon roads using image processing techniques and a model for controlling trafficsignals based on information received from images of vehicle present on theroads taken by video camera. In a video frame instead of calculating number ofvehicle we are going to calculate the area occupied by the vehicle on the roadin the terms of pixels. Variable traffic cycle and weighted time these twoparameters are considered for each road based on density of vehicle andsequence of control traffic lights.Keywords: Advance transportation system, trafficlight, image processing, edge detection, traffic density calculation.    I.

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                INTRODUCTION1Traffic congestion is when vehicle travel at slower speed because thereare more vehicle on than the road can handle this make trip times longer, andincrease queuing this is the major problem occur in day today life in bigcities. It is important to have a smart traffic control system to assure a safetransportation. The very first step to do that is to acquire condition oftraffic i.e density of vehicles present on the road. From different sensors wecan take the information of traffic congestion.

 Induction loop, infra-red light sensor, optical flow etc these examplesof sensors.  In day to day life imageprocessing techniques 14 has been very important and promising topic to dealwith traffic related problems because of its ease of maintenance and being moresmart as well as intelligent system. Different methods 2-5 have beenproposed to acquire traffic information. Most of the work detects edge of thevehicles and counts the number of vehicle present on the road.

However thedisadvantage of the method is that counting the number of vehicles may givewrong results when spaces between the vehicles on the road are very small (i.e.two cars very close to each other may be counted as one vehicle).

In this paper we are going to propose a methodology for determiningtraffic congestion on roads using image processing techniques and a model forcontrolling traffic signals based on information received from images ofvehicle present on the roads taken by video camera. In a video frame instead ofcalculating number of vehicle we are going to calculate the area occupied bythe vehicle on the road in the terms of pixels. Variable traffic cycle andweighted time these two parameters are considered for each road based ondensity of vehicle and sequence of control traffic lights.

    II. LITERATURE SURVEYMd. Munir Hasan, Gobinda Saha, Aminul Hoque and Md. Badruddoja Majumde ,In this paper they propose a method for determining traffic congestion on roadsusing image processing techniques and a model for controlling traffic signalsbased on information received from images of roads taken by video camera.

Theyextract traffic density which corresponds to total area occupied by vehicles onthe road in terms of total amount of pixels in a video frame instead ofcalculating number of vehicles. They set two parameters as output,variabletraffic cycle and weighted time for each road based on traffic density andcontrol traffic lights in a sequential manner.  Prashant Jadhav, PratikshaKelkar, Kunal Patil, and Snehal Thorat ,The fact is that, the population of city andnumbers of vehicles on the road are increasing day by day. With increasingurban population and hence the number of vehicles, need of controlling streets,highways and roads is major issue. The main reason behind today’s trafficproblem is the techniques that are used for traffic management. Today’s trafficmanagement system has no emphasis on live traffic scenario, which leads toinefficient traffic management systems. This project has been implemented byusing the Mat lab software and it aims to prevent heavy traffic congestion.

Moreover, for implementing this project Image processing technique is used.                                                                                       Vismay Pandit, Jinesh Doshi,Dhruv Mehta, Ashay Mhatre and Abhilash Janardhan, The simplest way for  controlling a traffic light uses timer for eachphase. Another way is to use.Electronic sensors in order to detect vehicles,and produce signal that cycles. We propose a system for controlling the trafficlight by image processing. The system will detectVehicles throughimages instead of using electronic sensors embedded in the pavement. A camerawill be installed alongside the traffic light.

 Omkar Ramdas Gaikwad, Anil Vishwasrao, Prof. KanchanPujari, Tejas Talathi  The main reason behind today’s traffic problem is the techniques that areused for traffic management. Today’s traffic management system has no emphasison live traffic scenario, which leads to inefficient traffic managementsystems. These traffic timers just show the preset time. This is like usingopen loop system. If we incorporate a closed loop system using camera, it ispossible to predict the exact time on traffic light timers. If the trafficlight timers are showing correct time to regulate the traffic, then the timewasted on unwanted green signals (green signal, when there is no traffic) willbe saved. Timer for every lane is the simplest way to control traffic.

And ifthose timers are predicting exact time then automatically the system will bemore efficient. This paper represents the project that has been implemented byusing the Matlab software and it aims to prevent heavy traffic congestion.  Thisproject does not actually measure the number of vehicles present on the road,but measures the area covered by vehicles on the road  A web camera is placed in a traffic lane thatwill capture images of the road on which we want to control traffic.

Then theseimages are efficiently processed to know the traffic density. According to theprocessed data from Matlab, the controller will send the command to the timerto show particular time on the signal to manage traffic.III.METHDOLOGY A)    PRE-PROCESSING: Pre-processing is a technique used to convertRGB color to gray color image. It is done by using luminance converter                        shown in belowequation.Is=0.2896*IR+0.

5870*IG+0.1140*IB        Is the grey level image IR, IG, IB   are the luminance in red, luminance in greenand luminance in blue. LCD 16X2                     16*2 LCD                     Camera         LED Panel 1                                                            LED Panel 1        Computer (with MATLAB  installed )                                                                    Figure 1:  Block Diagram B)    IMAGE ENHANCEMENT:  Better contrast anddetailed image are provided by enhancing an image compare to a non enhancedone. Some of image enhancement techniques are power-lawTransformation, linear method and Logarithmic method.  Among them, power law transformation methodis best approach which has the basic formula as shown below:                               V = K v? Where Vand v are I/O gray levels, ? & K is apositive constant (K=1). Therefore, deciding an accurate utilityof ? can play a pretentious action in image heighten process. For attain aGamma correction, the association.Between lightinput and output signals must be taken.

This is done by the following equation                         S (0) =K. (e) (E) S (0) = K. (e)(E) is output gain and K is the exposure time that is related to intensity andlinear vehicles.  C)    OBJECT DETECTION:    Edges of an image correspond to objectboundaries.

These edges are nothing but pixels where the change in brightnessmay occur and is calculated the behavior of image function in a neighboringpixel.   D)    EDGE DETECTION:       It is an image processingtechnique for finding the boundaries of object within image. In the detection unit weDetect the edges of image in totwo parts that is background and foreground image1)    After edge detection weget two images foreground and background image, by subtracting two images weget foreground object and background object 13.2)    To reduce the additivenoise and blurring effect which added by the processing of subtraction we usewiener filter. E)    MORPHOLOGICAL OPERATION:         There are two typesof morphological operation first is      morphological opening and morphological closing. In this project weperform morphological image closing to remove small holes within an image.1)     Tofill the holes in the objects with closed contours we perform flood fill operation6.

We get solid foreground image.2)     Nowwe convert gray scale image to binary image.                               Figure 2:  Street intersection  IV. CONCLUSION              In this paper, the image capturedby the camera from the road and after that captured videos are arranged inserial image. The number of cars has been counted by processing on the eachcaptured image. By setting the threshold value the number of cars exceeds thethreshold value the heavy traffic will be shown automatically. The advantagesof this new method include such as use of image processing over sensors, lowcost, easy setup and relatively good accuracy and speed.

Because this methodhas been implemented using Image Processing and Matlab software, production costsare low while achieving high speed and accuracy. REFERENCES1 Md.Munir Hasan, Gobinda Saha and Md. Badruddoja Majumder”Smart Traffic Control System with Application of Image Processing Techniques”Publish in 3rd InternationalConference on Informatics, Electronics & vision 2014. 2 D.

Beymer, P. McLauchlan, B. Coifman, and J. Malik, “Areal-time computer vision system for measuring traf?c parameters,” IEEE Conf.on Computer Vision and Pattern Recognition, pp. 495-501, 1997.  3 M.

Fathy, and M. Y.Siyal, “An image detection technique based on morphological edge detection andbackground differencing for realtime traf?c analysis,” Pattern RecognitionLetters, vol. 16, pp. 1321-1330, Dec. 1995.

  4 R. Cucchiara, M.Piccardi, and P. Mello, “Image analysis and rule-based reasoning for a traf?cmonitoring system,” IEEE Trans. on Intelligent Transportation Systems, Vol. 1,Issue 2, pp 119-130, 2000.  5 P. Choudekar, A.

K. Garg, S. Banerjee, M. K. Muju,”Implementation of image processing in real time traf?c light control,” IEEEConf.

on Electronics Computer Technology, Vol. 2, pp. 94-98, 2011. 6 P. Soille, Morphological Image Analysis:Principles and Applications, Springer-Verlag, 1999, pp. 173-174. 7 PrashantJadhav, Pratiksha Kelkar, Kunal Patil, Snehal Thorat” Smart TrafficControl System Using Image Processing” Publish in International ResearchJournal of Engineering and Technology (IRJET) Volume: 03 Issue: 03 | Mar-2016 8 OmkarRamdas Gaikwad, Anil Vishwasrao, Prof. Kanchan Pujari, Tejas Talathi,” ImageProcessing Based Traffic Light Control” International Journal of Science,Engineering and Technology Research (IJSETR) Volume 3, Issue 4, April 2014 9Vismay Pandit, Jinesh Doshi, Dhruv Mehta, AshayMhatre and Abhilash Janardhan.

” Smart Traffic Control System Using ImageProcessing” Publish in International Journal of Emerging Trends & Technology in ComputerScience (IJETTCS) Volume 3, Issue 1, January February2014  10 Image Processing Based Traffic LightControl OmkarGaikwad, Anil Vishwasrao, Prof. Kanchan Pujari, Tejas Talathi InternationalJournal of Science, Engineering and Technology Research (IJSETR) Volume 3,Issue 4, April 2014 11intelligent traffic light controller using embedded system Sayali Ambekar,Shraddha Jawalkar, Anagha Patil, Shweta Patil International Research Journal ofEngineering and Technology (IRJET) Volume: 04 Issue: 02 | Feb -2017 12Smart Traffic Lights Switching and Traffic Density Calculation using VideoProcessing   Anurag Kanungo,Ayush Sharma, ChetanSingla  Proceedings of 2014 RAECS UIETPanjab University Chandigarh, 06 – 08 March, 2014 13M. Piccardi, “Background subtraction techniques: a review,” IEEE InternationalConference on Systems, Man and Cybernetics 4, pp. 30993104, Oct. 2004.

 14V.Kastrinaki, M. Zervakis, and K. Kalaitzakis, “A survey of video processing techniques   applications”, Image and Vision Computing,vol. 21, pp. 359-381, Apr 1 2003.