Call for Paper - May 2023 Edition
IJCA solicits original research papers for the May 2023 Edition. Last date of manuscript submission is April 20, 2023. Read More

Moving Vehicle Detection: A Review

International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 87 - Number 15
Year of Publication: 2014
S. P. Patil
M. B. Patil

S P Patil and M B Patil. Article: Moving Vehicle Detection: A Review. International Journal of Computer Applications 87(15):35-37, February 2014. Full text available. BibTeX

	author = {S. P. Patil and M. B. Patil},
	title = {Article: Moving Vehicle Detection: A Review},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {87},
	number = {15},
	pages = {35-37},
	month = {February},
	note = {Full text available}


Moving vehicle detection is an essential process for Intelligent Transportation system. During the last decade, a large amount of work has been trying to produced output for this challenge; however, performances of most of them still fall far behind human perception. In this paper the object detection problem is studied, analyzing and reviewing the most important and newest techniques. We propose a classification of all these techniques into different categories according to their main principle and features. Moreover, study and point out their proposed methods, weather condition mentioned for the proposed methods and some other conditions like as jamming, shadow effects on the vehicles.


  • 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.
  • Melo, A. Naftel, A. Bernardino, and J. Santos-Victor, "Detection and classification of highway lanes using vehicle motion trajectories," IEEE Trans. Intell. Transp. Syst. , vol. 7, no. 2, pp. 188–200, Jun. 2006.
  • H. -Y. Cheng, B. -S. Jeng, P. -T. Tseng, and K. -C. Fan, "Lane detection with moving vehicles in the traffic scenes," IEEE Trans. Intell. Transp. Syst. ,vol. 7, no. 4, pp. 571–582, Dec. 2006.
  • 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.
  • J. Zhou, D. Gao, and D. Zhang, "Moving vehicle detection for automatic traffic monitoring," IEEE Trans. Veh. Technol. , vol. 56, no. 1, pp. 51–59, Jan. 2007.
  • 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.
  • Bing-Fei Wu, Fellow, IEEE, and Jhy-Hong Juang,"Adaptive vehicle detector approach for complex envoirnmets," Trans. Intell. Transp. Syst. , vol. 13, no. 2, pp. 817–827, Jun. 2012.