Moving Vehicles Detection Using Automatic Background Extraction
Vehicle detection is the critical step for highway monitoring. In this paper we propose background subtraction and edge detection technique for vehicle detection. This technique uses the advantages of both approaches. The practical applications approved the effectiveness of this method. This method consists of two procedures: First, automatic background extraction procedure, in which the background is extracted automatically from the successive frames; Second vehicles detection procedure, which depend on edge detection and background subtraction. Experimental results show the effective application of this algorithm. Vehicles detection rate was higher than 91%.
[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] Z. Kim and J. Malik,; "Fast vehicle detection with probabilistic feature
grouping and its application to vehicle tracking",. Ninth IEEE
International Conference on Computer Vision, 13-16 Oct. 2003, pp. 524
- 531.
[3] 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.
[4] T. Zhao, and R. Nevatia, "Car detection in low resolution aerial image,"
Image and Vision Computing, vol. 21, 18 march 2003, pp. 693 - 703.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[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] Z. Kim and J. Malik,; "Fast vehicle detection with probabilistic feature
grouping and its application to vehicle tracking",. Ninth IEEE
International Conference on Computer Vision, 13-16 Oct. 2003, pp. 524
- 531.
[3] 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.
[4] T. Zhao, and R. Nevatia, "Car detection in low resolution aerial image,"
Image and Vision Computing, vol. 21, 18 march 2003, pp. 693 - 703.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
@article{"International Journal of Electrical, Electronic and Communication Sciences:49400", author = "Saad M. Al-Garni and Adel A. Abdennour", title = "Moving Vehicles Detection Using Automatic Background Extraction", abstract = "Vehicle detection is the critical step for highway monitoring. In this paper we propose background subtraction and edge detection technique for vehicle detection. This technique uses the advantages of both approaches. The practical applications approved the effectiveness of this method. This method consists of two procedures: First, automatic background extraction procedure, in which the background is extracted automatically from the successive frames; Second vehicles detection procedure, which depend on edge detection and background subtraction. Experimental results show the effective application of this algorithm. Vehicles detection rate was higher than 91%.
", keywords = "Image processing, Automatic background extraction, Moving vehicle detection.", volume = "2", number = "12", pages = "2649-5", }
