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Design and Implementation of a Real-Time Vehicle's Model Recognition System  

Choi Tae-Wan (진주산업대학교 메카트로닉스공학과)
Abstract
This paper introduces a simple but effective method for recognizing vehicle models corresponding to each maker by information and images for moving vehicles. The proposed approach is implemented by combination of the breadth detection mechanism using the vehicle's pressure, exact height detection by a laser scanning, and license plate recognition for classifying specific vehicles. The implemented system is therefore capable of robust classification with real-time vehicle's moving images and established sensors. Simulation results using the proposed method on synthetic data as well as real world images demonstrate that proposed method can maintain an excellent recognition rate for moving vehicle models because of image acquisition by 2-D CCD and various image processing algorithms.
Keywords
Vehicle recognition; classification; license plate recognition; image processing;
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1 H. Murase and S. Nayar, 'Visual Learning and Recognition of 3D Objects from Appearance,' Ini'l J. of Computer Vision, vol. 14, pp. 5-24,1995   DOI
2 Xia Limin, 'Vehicle Shape Recovery and Recognition Using Generic Models,' Proc. of the 4th World Congress on Intelligent Control and Automation, pp. 1055-1059,2002
3 강현인, 최태완, '압력식 차폭감지장치,' 특허출원 번호 10-2005-0043455, May, 2004
4 Matrox Co., Ltd., http://www.matrox.com
5 Richard O. Duda, Peter E. Hart, David G. Stork, Pattern Classification, Wiley Inter-Science, 2001
6 W. Hwang and H. Ko, 'Real-time Vehicle Recognition Using Local Feature Extraction,' Electronic Letters, vol. 37, no. 7, pp. 424-425, Mar. 2001   DOI   ScienceOn
7 Christoph Bush, Ralf Dorner, Christian Freytag, Heike Ziegler, 'Feature Based Recognition of Traffic Video Streams for Online Route Tracing,' Proc. of the IEEE Conf. on Vehicle Technology Conference, pp. 1790-1794,1999
8 Gonzalez and Woods, Digital Image Processing, Prentice Hall, 2002
9 Wei Wu, Zang QiSen, and Wang Mingjun, 'A Method of Vehicle Classification Using Models and Neural Networks,' Proc. of the IEEE Conf. on Vehicle Technology Conference, vol. 4, pp. 3022-3026,2001
10 Ryad Benosman and Sing Bing Kang, Panoramic Vision, Springer, 2001
11 A. Schanz, C. Knoeppel, and B. Michaelis, 'Robust Vehicle Detection at Large Distance Using Low Resolution Camera,' Proc. of the IEEE Intelligent Vehicles Symposium, pp. 267-272,2000
12 Kohtaro Ohba and Kasushi Ikeuchi, 'Detectability, Uniqueness, and Reliability of Eigen Windows for Stable Verification of Partially Occluded Objects,' IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 19, no. 9, pp. 1043-1048, 1997   DOI   ScienceOn
13 R. A. Lotufo, A. D. Morgan, and A. S. Johnson, 'Automatic number-plate recognition,' IEE Colloquium on Image Analysis for Transport Applications, Feb. 1990
14 Masataka Kagesawa, Shinichi Ueno, Katsushi Ikeuchi, and Hiroshi Kashiwagi, 'Local-Feature Based Vehicle Recognition in Infrared Images Using Parallel Vision Board,' Proc. of the IEEE Int'l Conf. on Intelligent Robots and Systems, pp. 1828-1833, 1999
15 SICK, Analogue Distance Sensors Data Sheet: DT60
16 Choudhury A. Rahman, Wael Badawy, and Ahmad Radmanesh, 'A Real Time Vehicle's License Plate Recognition System,' Proc. of the IEEE Conf. on Advanced Vuleo and Signal Based Surveillance, 2003
17 Neuricam, Nmnber Plate Recognition System NC6000 Data Sheet, http://www.neuricam.com. 2002