Intelligent Video-Based Monitoring of Freeway Traffic
Freeways are originally designed to provide high
mobility to road users. However, the increase in population and
vehicle numbers has led to increasing congestions around the world.
Daily recurrent congestion substantially reduces the freeway capacity
when it is most needed. Building new highways and expanding the
existing ones is an expensive solution and impractical in many
situations. Intelligent and vision-based techniques can, however, be
efficient tools in monitoring highways and increasing the capacity of
the existing infrastructures. The crucial step for highway monitoring
is vehicle detection. In this paper, we propose one of such
techniques. The approach is based on artificial neural networks
(ANN) for vehicles detection and counting. The detection process
uses the freeway video images and starts by automatically extracting
the image background from the successive video frames. Once the
background is identified, subsequent frames are used to detect
moving objects through image subtraction. The result is segmented
using Sobel operator for edge detection. The ANN is, then, used in
the detection and counting phase. Applying this technique to the
busiest freeway in Riyadh (King Fahd Road) achieved higher than
98% detection accuracy despite the light intensity changes, the
occlusion situations, and shadows.
[1] S. Gupte, O. Masoud, R. Martin, and N. Papanikolopoulos, "Detection
and classification of vehicles," IEEE Trans. Intelligent Transportation
Systems, vol. 3, no. 1, March 2002, pp. 37 - 47.
[2] H. Zhiwei, L. Jilin, and L. Peihong, "New method of background update
for video-based vehicle detection," in Proc. 7th Int. IEEE Conf.
Intelligent Transportation Systems, Oct. 2004, pp. 580 - 584.
[3] R. Rad, and M. Jamzad, "Real time classification and tracking of
multiple vehicles in highways," Pattern Recognition Letters, vol. 26,
no. 10, 15 July 2005, pp. 1597-1607.
[4] Y. Zhang, P. Shi, , E. Jones, and Q. Zhu, "Robust background image
generation and vehicle 3D detection and tracking," in Proc. 7th Int.
IEEE Conf. Intelligent Transportation Systems, 3-6 Oct. 2004, pp. 12 -
16.
[5] B. Chen, Y. Lei, and W. Li, "A novel background model for real-time
vehicle detection," in Proc. 7th Int. IEEE Conf. Intelligent
Transportation Systems, vol. 2, 31 Aug.- 4 Sept. 2004, pp. 1276 - 1279.
[6] X. Ji, Z. Wei, and Y. Feng, "Effective vehicle detection technique for
traffic surveillance systems," Journal of Visual Communication and
Image Representation, vol. 17, no. 3, Jun. 2006, pp. 647-658.
[7] H. Schneiderman, and T. Kanade, "A statistical method for 3D object
detection applied to faces and cars," IEEE Conf. Computer Vision and
Pattern Recognition, vol.1, 13-15 June 2000, pp. 746 - 751.
[8] T. Zhao, and R. Nevatia, "Car detection in low resolution aerial image,"
Image and Vision Computing, vol. 21, 18 march 2003, pp. 693 - 703.
[9] A. Rajagopalan, P. Burlina, and R. Chellappa, "Higher order statistical
learning for vehicle detection in images," in Proc. 7th Int. IEEE Conf.
Computer Vision, vol. 2, 20-27 Sept. 1999, pp. 1204 -1209.
[10] S. Hinz, "Detection and counting of cars in aerial images," in Proc. Int.
Conf. Image Processing, vol. 3, 14-17 Sept. 2003, pp. 997-1000.
[11] R. Ruskone, L. Guigues, S. Airault, and O. Jamet, "Vehicle detection on
aerial images: a structural approach," in Proc. 13th Int. IEEE Conf.
Pattern Recognition, vol. 3, 25-29 Aug. 1996, pp. 900 - 904.
[12] C. Hoffman, T. Dang, and C. Stiller, "Vehicle detection fusing 2D visual
features," in Proc. IEEE Intelligent Vehicles Symposium, 14-17 June
2004, pp. 280 - 285.
[13] S. Mantri, and D. Bullock,"A neural network based vehicle detection
and tracking system," in Proc. 27th Southeastern Symposium on System
Theory, 12-14 March 1995, pp. 279 - 283.
[14] D. Ha, J. Lee, and Y. Kim, "Neural-edge-based vehicle detection and
traffic parameter extraction," Image and vision computing, vol. 22, 2004,
pp. 899-907.
[15] Z. Liu, X. Li, and X. Leung, "Fuzzy measures for vehicle detection,"
The 10th IEEE Int. Conf. Fuzzy Systems, vol. 2, 2-5 Dec. 2001, pp. 848 -
851.
[16] R. Flores, "Evaluation of the use of high-resolution satellite imagery in
transportation applications," final report prepared for northland
advanced transportation systems research laboratories, Dept. Electrical
and Computer Eng., Univ. Duluth, March 2004, pp. 10-50.
[17] R. Gonzalez, R. Woods, and S. Eddins, Digital image processing using
Matlab. New Jersey, Pearson Prentice-Hall, Inc., 2004. ch. 3.
[18] R. Rao, and A. Bopardikar, "Wavelet Transform Introduction to Theory
and Applications," Addison Wesley, Longman, Inc., Massachsetts,
USA, 1998. ch. 10.
[19] L. Smith, "A tutorial on principle component analysis,"
http://www.cs.otago.ac.nz/ cosc453 /student_tutorials.
[20] A. Suleiman, and O. Khalifa, "Real time vehicle license plate
recognition," in Proc. Int. Conf. Computer and Communication
Engineering, ICCCE-06,Kuala Lumpur, Malaysia, II, 9-11 May 2006,
pp. 1072 - 1077.
[21] S. Hayken, Neural Networks A comprehensive Foundation. New York,
Macmillan, 1994. ch. 7.
[22] A. Al-Ghadeer, Automatic Facial Expressions Recognition Using
Artificial Intelligence. M.S. thesis, Dept. Electrical Eng., King Saud
Univ., K.S.A., 2004.
[23] A. May, Traffic Flow Fundamentals. New Jersey, Pearson Prentice-
Hall, Inc., 1990, ch. 6, 7.
[1] S. Gupte, O. Masoud, R. Martin, and N. Papanikolopoulos, "Detection
and classification of vehicles," IEEE Trans. Intelligent Transportation
Systems, vol. 3, no. 1, March 2002, pp. 37 - 47.
[2] H. Zhiwei, L. Jilin, and L. Peihong, "New method of background update
for video-based vehicle detection," in Proc. 7th Int. IEEE Conf.
Intelligent Transportation Systems, Oct. 2004, pp. 580 - 584.
[3] R. Rad, and M. Jamzad, "Real time classification and tracking of
multiple vehicles in highways," Pattern Recognition Letters, vol. 26,
no. 10, 15 July 2005, pp. 1597-1607.
[4] Y. Zhang, P. Shi, , E. Jones, and Q. Zhu, "Robust background image
generation and vehicle 3D detection and tracking," in Proc. 7th Int.
IEEE Conf. Intelligent Transportation Systems, 3-6 Oct. 2004, pp. 12 -
16.
[5] B. Chen, Y. Lei, and W. Li, "A novel background model for real-time
vehicle detection," in Proc. 7th Int. IEEE Conf. Intelligent
Transportation Systems, vol. 2, 31 Aug.- 4 Sept. 2004, pp. 1276 - 1279.
[6] X. Ji, Z. Wei, and Y. Feng, "Effective vehicle detection technique for
traffic surveillance systems," Journal of Visual Communication and
Image Representation, vol. 17, no. 3, Jun. 2006, pp. 647-658.
[7] H. Schneiderman, and T. Kanade, "A statistical method for 3D object
detection applied to faces and cars," IEEE Conf. Computer Vision and
Pattern Recognition, vol.1, 13-15 June 2000, pp. 746 - 751.
[8] T. Zhao, and R. Nevatia, "Car detection in low resolution aerial image,"
Image and Vision Computing, vol. 21, 18 march 2003, pp. 693 - 703.
[9] A. Rajagopalan, P. Burlina, and R. Chellappa, "Higher order statistical
learning for vehicle detection in images," in Proc. 7th Int. IEEE Conf.
Computer Vision, vol. 2, 20-27 Sept. 1999, pp. 1204 -1209.
[10] S. Hinz, "Detection and counting of cars in aerial images," in Proc. Int.
Conf. Image Processing, vol. 3, 14-17 Sept. 2003, pp. 997-1000.
[11] R. Ruskone, L. Guigues, S. Airault, and O. Jamet, "Vehicle detection on
aerial images: a structural approach," in Proc. 13th Int. IEEE Conf.
Pattern Recognition, vol. 3, 25-29 Aug. 1996, pp. 900 - 904.
[12] C. Hoffman, T. Dang, and C. Stiller, "Vehicle detection fusing 2D visual
features," in Proc. IEEE Intelligent Vehicles Symposium, 14-17 June
2004, pp. 280 - 285.
[13] S. Mantri, and D. Bullock,"A neural network based vehicle detection
and tracking system," in Proc. 27th Southeastern Symposium on System
Theory, 12-14 March 1995, pp. 279 - 283.
[14] D. Ha, J. Lee, and Y. Kim, "Neural-edge-based vehicle detection and
traffic parameter extraction," Image and vision computing, vol. 22, 2004,
pp. 899-907.
[15] Z. Liu, X. Li, and X. Leung, "Fuzzy measures for vehicle detection,"
The 10th IEEE Int. Conf. Fuzzy Systems, vol. 2, 2-5 Dec. 2001, pp. 848 -
851.
[16] R. Flores, "Evaluation of the use of high-resolution satellite imagery in
transportation applications," final report prepared for northland
advanced transportation systems research laboratories, Dept. Electrical
and Computer Eng., Univ. Duluth, March 2004, pp. 10-50.
[17] R. Gonzalez, R. Woods, and S. Eddins, Digital image processing using
Matlab. New Jersey, Pearson Prentice-Hall, Inc., 2004. ch. 3.
[18] R. Rao, and A. Bopardikar, "Wavelet Transform Introduction to Theory
and Applications," Addison Wesley, Longman, Inc., Massachsetts,
USA, 1998. ch. 10.
[19] L. Smith, "A tutorial on principle component analysis,"
http://www.cs.otago.ac.nz/ cosc453 /student_tutorials.
[20] A. Suleiman, and O. Khalifa, "Real time vehicle license plate
recognition," in Proc. Int. Conf. Computer and Communication
Engineering, ICCCE-06,Kuala Lumpur, Malaysia, II, 9-11 May 2006,
pp. 1072 - 1077.
[21] S. Hayken, Neural Networks A comprehensive Foundation. New York,
Macmillan, 1994. ch. 7.
[22] A. Al-Ghadeer, Automatic Facial Expressions Recognition Using
Artificial Intelligence. M.S. thesis, Dept. Electrical Eng., King Saud
Univ., K.S.A., 2004.
[23] A. May, Traffic Flow Fundamentals. New Jersey, Pearson Prentice-
Hall, Inc., 1990, ch. 6, 7.
@article{"International Journal of Electrical, Electronic and Communication Sciences:49630", author = "Saad M. Al-Garni and Adel A. Abdennour", title = "Intelligent Video-Based Monitoring of Freeway Traffic", abstract = "Freeways are originally designed to provide high
mobility to road users. However, the increase in population and
vehicle numbers has led to increasing congestions around the world.
Daily recurrent congestion substantially reduces the freeway capacity
when it is most needed. Building new highways and expanding the
existing ones is an expensive solution and impractical in many
situations. Intelligent and vision-based techniques can, however, be
efficient tools in monitoring highways and increasing the capacity of
the existing infrastructures. The crucial step for highway monitoring
is vehicle detection. In this paper, we propose one of such
techniques. The approach is based on artificial neural networks
(ANN) for vehicles detection and counting. The detection process
uses the freeway video images and starts by automatically extracting
the image background from the successive video frames. Once the
background is identified, subsequent frames are used to detect
moving objects through image subtraction. The result is segmented
using Sobel operator for edge detection. The ANN is, then, used in
the detection and counting phase. Applying this technique to the
busiest freeway in Riyadh (King Fahd Road) achieved higher than
98% detection accuracy despite the light intensity changes, the
occlusion situations, and shadows.", keywords = "Background Extraction, Neural Networks, VehicleDetection, Freeway Traffic.", volume = "2", number = "8", pages = "1559-7", }
