Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30067
Real Time Speed Estimation of Vehicles

Authors: Azhar Hussain, Kashif Shahzad, Chunming Tang


this paper gives a novel approach towards real-time speed estimation of multiple traffic vehicles using fuzzy logic and image processing techniques with proper arrangement of camera parameters. The described algorithm consists of several important steps. First, the background is estimated by computing median over time window of specific frames. Second, the foreground is extracted using fuzzy similarity approach (FSA) between estimated background pixels and the current frame pixels containing foreground and background. Third, the traffic lanes are divided into two parts for both direction vehicles for parallel processing. Finally, the speeds of vehicles are estimated by Maximum a Posterior Probability (MAP) estimator. True ground speed is determined by utilizing infrared sensors for three different vehicles and the results are compared to the proposed algorithm with an accuracy of ± 0.74 kmph.

Keywords: Defuzzification, Fuzzy similarity approach, lane cropping, Maximum a Posterior Probability (MAP) estimator, Speed estimation

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF


[1] H. A. Rahim, U. U. Sheikh, R. B. Ahmad and A. S. M. Zain " Vehicle Velocity Estimation for Traffic Surveillance System" World Academy of Science Sep, 2010.
[2] Cristina Maduro, Katherine Batista and Jorge Batista "Estimating traffic intensity using profile images on rectified images" in IEEE Proceedings Sep ,2009.
[3] Sen-Ching, S. Cheung and Chandrika Kamath "Robust techniques for background subtraction in urban traffic video "Proceedings of the SPIE, Volume 5308, pp. 881-892 ,Jan 2004.
[4] Gholam ali rezai , Javad mohamadi "Vehicle Speed Estimation Based On The Image" in IEEE proceedings of 4th international conference Sciences of Electronic, Technologies of Information and Telecommunications March, 2007 .
[5] Lazaros Grammatikopoulos, George Karras, Elli Petsa (GR), "Automatic estimation of vehicle speed from uncalibrated video sequences" in Proceedings of international symposium on modern technologies, education and professional practice in geodesy and related fields sofia, 03 - 04 Nov, 2005
[6] P. KaewTraKulPong and R. Bowden, "An improved adaptive background mixture model for real-time tracking with shadow detection," in Proceedings of the 2nd European Workshop on Advanced Video-Based Surveillance Systems, Sept. 2001.
[7] M. Harville, "A framework for high-level feedback to adaptive, per-pixel, mixture-of-Gaussian background models," in Proceedings of the Seventh European Conference on Computer Vision, Part III, pp. 543{60, (Copenhagen, Denmark), May 2002.
[8] D. R. Magee, "Tracking multiple vehicles using foreground, background, and motion models," in Proceedings of the Statistical Methods in Video Processing Workshop, pp. 12, (Copenhagen, Denmark), June 2002.
[9] R. Cucchiara, M. Piccardi, and A. Prati, "Detecting moving objects, ghosts, and shadows in video streams," IEEE Transactions on Pattern Analysis and Machine Intelligence 25, pp. 1342, Oct 2003.
[10] L. Fuentes and S. Velastin, "From tracking to advanced surveillance," in Proceedings of IEEE International Confer erence on Image Processing, (Barcelona, Spain), Sept 2003.
[11] T. N. Schoepflin and D. J. Dailey, "Dynamic camera calibration of roadside traffic management cameras for vehicle speed estimation," IEEE Trans. on Intelligent Transportation Systems, vol. 4, no. 2,pp. 90- 98, June 2003. 13. N. Oliver, B. Rosario, and A. Pentland, "A Bayesian computer vision system for modeling human interactions,"IEEE Transactions on Pattern Analysis and Machine Intelligence 22, pp. 831, Aug 2000.
[12] A. Prati, I. Mikic, M. Trivedi, and R. Cucchiara, "Detecting moving shadows: algorithms and evaluation," IEEE Transactions on Pattern Analysis and Maching Intelligence 25, pp. 923, July 2003.
[13] B. Gloyer, H. Aghajan, K.-Y. Siu, and T. Kailath, "Video-based freeway monitoring system using recursive vehicle tracking," in Proceedings of SPIE, 2421, pp. 173, Feb 1995.
[14] Q. Zhou and J. Aggarwal, "Tracking and classifying moving objects from videos," in Proceedings of IEEE Workshop on Performance Evaluation of Tracking and Surveillance, 2001.
[15] K.-P. Karmann and A. Brandt, "Moving object recognition using and adaptive background memory," in Time-Varying Image Processing and Moving Object Recognition, V. Cappellini, ed., 2, pp. 307, Elsevier Science Publishers B.V., 1990.
[16] Detection theory: applications and digital signal processing, Ralph Hippenstiel, (CRC Press 2002).
[17] P. W. Power and J. A. Schoonees, "Understanding background mixture models for foreground segmentation," in Proceedings Image and Vision Computing New Zealand, pp. 271, (Auckland, New Zealand), Nov 2002.
[18] D.-S. Lee, J. Hull, and B. Erol, "A Bayesian framework for gaussian mixture background modeling," in Proceedings of IEEE International Confererence on Image Processing, (Barcelona, Spain), Sept 2003.