Traffic control is considered as one of the fastest
developing technologies in the world. In India with the
growing number of vehicles, traffic jam at junctions has
become a serious issue. Normally Traffic police, Timers,
Electronic sensors are used to control the traffic jam. But
nowadays, image processing techniques are used to control
traffic. This paper discuss about the various traffic control
techniques and their comparisons. In order to reduce the
traffic problems real time traffic control system using image
processing is very advantageous.
Detty M Panicker : received her B.Tech
(Computer Science & Engineering)
degree from University of Kerala,
Trivandrum in 2014. She is currently
pursuing her Masters in Computer
Science & Engineering from University
of Kerala. Her research interests
focuses on image processing, data
mining, and image mining.
Radhakrishnan B : is working as Asst.
Professor in computer science
department. He has more than 14 years
experience in teaching and has
published papers on data mining and
image processing. His research
interests include image processing, data
mining, image mining.
Traffic light
Image Processing
Image Matching
Edge Detection
Background Subtraction
In this paper we discussed about the existing traffic
control system and their drawback. To overcome from
those drawbacks we can build a flexible traffic light
control system based on traffic density. To find the traffic
density edge detection techniques can be used. Gaussian
based edge detection is sensitive to noise. The canny edge
detection gives best performance even in noise condition
compare to other first order edge detection and more
costly as compared to Sobel, Prewitt and Robert’s
operator. Improve the performance of background
subtraction, Traffic Queue Detection Algorithm,
segmentation, morphological operation etc. A best edge
detection algorithm is necessary to provide an errorless
solution or fuzzy logic, morphological based edge
detection technique for regulating traffic light system
based on traffic density to save the time and to reduce
operating cost.
[1] Chandrasekhar. M, Saikrishna. C, Chakradhar. B,
Phaneendra Kumar. P & Sasanka. C ,” Traffic Control
Using Digital Image Processing “International Journal
of Advances in Electrical & Electronics Engineering
(IJAEEE) , volume-2, issue-5, 2013.
[2] Pallavi Choudekar, Sayanti Banerjee,
Prof.M.K.Muju,” Real Time Traffic Light Control
Using Image Processing “,Indian Journal Of Computer
Science And Engineering (IJCSE), Vol. 2 No. 1, P6-
10.
[3] Kavya P Walad, Jyothi Shetty,” Traffic Light Control
System Using Image Processing”,International Journal Of Innovative Research In Computer And
Communication Engineering.Vol.2,Special Issue
5,October 2014.
[4] P.Srinivas1 ,Y.L. Malathilatha, Dr. M.V.N.K Prasad,”
Image Processing Edge Detection Technique used for
Traffic Control Problem”, International Journal of
Computer Science and Information Technologies, Vol.
4 (1) , 2013, 17 – 20.
[5] Meru A.V., Mujawar I.I,” Computer Vision Based
Vehicles Detection And Traffic Control For Four Way
Road “International Journal Of Current Engineering
And Scientific Research (IJCESR)Volume-2, Issue-6,
2015.
[6] Mahesh C. Pawaskar, N. S.Narkhede and Saurabh S.
Athalye” Detection of Moving Object Based On
Background Subtraction”, International Journal of
Emerging Trends & Technology in Computer Science
(IJETTCS), Volume 3, Issue 3, May-June 2014.
[7] Trupti A. Chopkar , Shashikant Lahade, “Real Time
Detection of Moving Object Based On FPGA,
International Journal of Scientific Engineering and
Research (IJSER), Volume 3 Issue 11, November
2015.
[8] Fathy M, Siyal K,”Real-time image processing
approach to measure traffic queue parameters
“,Vision, Image and Signal Processing, IEEE
Proceedings ,Volume:142, Issue 5.
[9] Alok T, Kumar M, Kumar L, Kumar S,” Image
Processing Applied To Traffic Queue Detection
Algorithm”, International Journal of Applied
Engineering Research (IJAER),Vol.7, (2012).
[10] Bharti Sharma, Vinod Kumar Katiyar, Arvind Kumar
Gupta, Akansha Singh,” The Automated Vehicle
Detection of Highway Traffic Images by Differential
Morphological Profile “Journal of Transportation
Technologies, 2014, 4, 150-156.
[11] Pradip Singh Maharjan, Ajay Kumar Shrestha,”
Automatic Vehicle Detection And Road Traffic
Congestion Mapping With Image Processing
Technique”, International Journal for Computer
Applications (IJCA), Vol 114- No.16, March 2015.
[12] Siddharthy S, “Detecting the object by Image
Segmentation Algorithm using FPGA”, International
Journal of VLSI and Signal Processing Application,
Vol.2, Issue 1, Feb 2012.
[13] Hyeok Jang, In-Su Won, and Dong-Seok Jeong,”
Automatic Vehicle Detection and Counting Algorithm
“,IJCSNS International Journal of Computer Science
and Network Security, VOL.14 No.9, September
2014.
[14] G. Salvi,” An Automated Vehicle Counting System
Based on Blob Analysis for Traffic Surveillance “,
International Conference on Image Processing,
Computer Vision, and Pattern Recognition (IPCV),
2012,1-6.
[15] Vikramaditya Dangi, Amol Parab, Kshitij Pawar
& S.S Rathod "Image Processing Based Intelligent
Traffic Controller", Undergraduate Academic
Research Journal (UARJ), ISSN : 2278 – 1129,
Volume-1, Issue-1, 2012.
[16] V. Parthasarathi1, M. Surya, B. Akshay, K. Murali
Siva and Shriram K. Vasudevan,” Smart Control of
Traffic Signal System using Image Processing”, Indian
Journal of Science and Technology, Vol 8(16), 64622,
July 2015.
[17] D. Koller, J. Weber, T. Huang, J. Malik, J.
Ogasawara, G. Rao, and S. Russell, “Towards robust
automatic traffic scene analysis in realtime,” in Proc.
12th International Conference on Computer Vision
and Image Processing., 1994, vol. 1, pp. 126–131.
[18] Sidhu J, Verma B, Dr Sardan H. K., “Real time
image processing design issues”, National conference
on computational instrumentation 2010, CISCO
Chandigarh.
[19] http://www.peterkovesi.com/projects/ Phase Based
Feature Detection and Phase Congruency/
[20] https://en.wikipedia.org/wiki/
