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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:53238", author = "Azhar Hussain and Kashif Shahzad and Chunming Tang", title = "Real Time Speed Estimation of Vehicles", abstract = "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", volume = "6", number = "1", pages = "31-5", }
