A Survey and Comparative Study of Real Time Vehicle Detection Methods for Road Traffic Applications

Print
 
Download Full Text
IJCA Proceedings on National Conference on Digital Image and Signal Processing
© 2016 by IJCA Journal
NCDISP 2016 - Number 1
Year of Publication: 2016
Authors:
Swati N. Divatankar
Umesh N. Hivarkar

Swati N Divatankar and Umesh N Hivarkar. Article: A Survey and Comparative Study of Real Time Vehicle Detection Methods for Road Traffic Applications. IJCA Proceedings on National Conference on Digital Image and Signal Processing NCDISP 2016(1):28-31, August 2016. Full text available. BibTeX

@article{key:article,
	author = {Swati N. Divatankar and Umesh N. Hivarkar},
	title = {Article: A Survey and Comparative Study of Real Time Vehicle Detection Methods for Road Traffic Applications},
	journal = {IJCA Proceedings on National Conference on Digital Image and Signal Processing},
	year = {2016},
	volume = {NCDISP 2016},
	number = {1},
	pages = {28-31},
	month = {August},
	note = {Full text available}
}

Abstract

Vehicle count is increasing by the day in urban area. Vehicle detection plays an important role in road traffic applications. By using vehicle detection methods different traffic parameters such as vehicle speed, density, volume, traffic flow rate, travelling time, congestion level can be calculated and these methods can be applied for vehicle tracking, vehicle classification, parking area monitoring , road traffic monitoring and management etc. Various real time vehicle detection methods have been proposed by researchers. The objective of this paper is to present the various approaches for real time vehicle detection using image processing, also to provide comparison of these methods along with pros and cons of each method.

References

  • Meru A. V, Mujawar I. I , "Computer vision based vehicles detection and counting for four way traffic", International Journal of Advanced Research in Computer Science and Software Engineering, ISSN 2277 128X, Volume 5, Issue 2, February 2015
  • Dan Yang, Yantao Chen, Richen Liu, "Vehicle Detection in Video Based on the Framework of Kernel Density Estimation", The 2013 AASRI Winter International Conference on Engineering and Technology (AASRI-WIET 2013)
  • Deng-Yuan Huang, Chao-Ho Chen, "Feature-Based Vehicle Flow Analysis and Measurement for a Real-Time Traffic Surveillance System", Journal of Information Hiding and Multimedia Signal Processing, ISSN 2073-4212,Volume 3, Number 3, July 2012
  • G. C. De Silva, "Automation of Traffic Flow Measurement Using Video Images," Master of Engineering, University of Moratuwa, Sri Lanka, 2001
  • Ankita Rawat, Anuj Saxena, "A Review and Comparison of Well-Known Methods for Object Detection and Tracking in Videos" International Journal of Advanced Research in Computer Science and Software Engineering 4(5), pp. 282-287, May - 2014
  • 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
  • Abhishek Kumar Chauhan, Prashant Krishan, " Moving Object Tracking using Gaussian Mixture Model and Optical Flow", International Journal of Advanced Research in Computer Science and Software Engineering, ISSN 2277 128X, Volume 3, Issue 4, April 2013
  • Y. Wang, "Joint random field model for all-weather moving vehicle detection," IEEE Trans. Image Process, vol. 19, no. 9, pp. 2491–2501,Sep. 2010
  • L. -W. Tsai, J. -W. Hsieh and K. -C. Fan, "Vehicle detection using normalized color and edge map," IEEE Trans. Image Process. , vol. 16, no. 3, pp. 850–864, Mar. 2007
  • W. Zhang, Q. M. J. Wu, and X. Yang,"Multilevel framework to detect and handle vehicle occlusion," IEEE Trans. Intell. Transp. Syst. , vol. 9, no. 1, pp. 161–174, Mar. 2008
  • N. K. Kanhere and S. T. Birchfield, "Real-time incremental segmentation and tracking of vehicles at low camera angles using stable features," IEEE Trans. Intell. Transp. Syst. , vol. 9, no. 1, pp. 148–160, Mar. 2008.
  • M. Vargas, J. M. Milla, S. L. Toral, and F. Barrero, "An enhanced background estimation algorithm for vehicle detection in urban traffic scenes,"IEEE Trans. Veh. Technol. , vol. 59, no. 8, pp. 3694–3709, Oct. 2010
  • R. Cucchiara, M. Piccardi, and P. Mello, "Image analysis and rule-based reasoning for a traffic monitoring system," IEEE Trans. Intell. Transp. Syst. , vol. 1, no. 2, pp. 119–130, Jun. 2000
  • Paygude S. S. , Dr. VyasVibha, and Chaple Manisha, "Vehicle Detection and Tracking using the Optical Flow and Background Subtraction", Proc. of Int. Conf. on Advances in Computer Science and Application, Elsevier, 2013A. Gyaourova, C. Kamath, S. -C. Cheung,"Block matching for Object Tracking", UCRL-TR-200271, 2003
  • Hasegawa, O. and T. Kanade, "Type Classification, Color Estimation, and Specific Target Detection of Moving Targets on Public Streets", Machine Vision and Applications, Vol. 16, No. 2, pp. 116-121, 2005
  • D. Beymer, P. McLauchlan, B. Coifman and J. Malik, "A Real-time Computer Vision System for Measuring Traffic Parameters", In Proceeding. IEEE Conference. Computer Vision and Pattern Recognition, Puerto Rico, pp. 496–501, June, 1997
  • J. C. Lai, S. S. Huang, and C. C. Tseng, "Image-based vehicle tracking and classification on the highway", Proc. of International Conference on Green Circuits and Systems, pp. 666-670, 2010