Commenced in January 2007
Paper Count: 30184
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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1328524Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1500
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 T. Zhao, and R. Nevatia, "Car detection in low resolution aerial image," Image and Vision Computing, vol. 21, 18 march 2003, pp. 693 - 703.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 R. Gonzalez, R. Woods, and S. Eddins, Digital image processing using Matlab. New Jersey, Pearson Prentice-Hall, Inc., 2004. ch. 3.
 R. Rao, and A. Bopardikar, "Wavelet Transform Introduction to Theory and Applications," Addison Wesley, Longman, Inc., Massachsetts, USA, 1998. ch. 10.
 L. Smith, "A tutorial on principle component analysis," http://www.cs.otago.ac.nz/ cosc453 /student_tutorials.
 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.
 S. Hayken, Neural Networks A comprehensive Foundation. New York, Macmillan, 1994. ch. 7.
 A. Al-Ghadeer, Automatic Facial Expressions Recognition Using Artificial Intelligence. M.S. thesis, Dept. Electrical Eng., King Saud Univ., K.S.A., 2004.
 A. May, Traffic Flow Fundamentals. New Jersey, Pearson Prentice- Hall, Inc., 1990, ch. 6, 7.