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

실시간 차종인식 시스템의 설계 및 구현

  • 최태완 (진주산업대학교 메카트로닉스공학과)
  • Published : 2006.05.01


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.


  1. R. A. Lotufo, A. D. Morgan, and A. S. Johnson, 'Automatic number-plate recognition,' IEE Colloquium on Image Analysis for Transport Applications, Feb. 1990
  2. Neuricam, Nmnber Plate Recognition System NC6000 Data Sheet, 2002
  3. 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
  4. 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
  5. 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
  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
  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. 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
  9. 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
  10. 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
  11. 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
  12. 강현인, 최태완, '압력식 차폭감지장치,' 특허출원 번호 10-2005-0043455, May, 2004
  13. SICK, Analogue Distance Sensors Data Sheet: DT60
  14. Matrox Co., Ltd.,
  15. Richard O. Duda, Peter E. Hart, David G. Stork, Pattern Classification, Wiley Inter-Science, 2001
  16. Ryad Benosman and Sing Bing Kang, Panoramic Vision, Springer, 2001
  17. Gonzalez and Woods, Digital Image Processing, Prentice Hall, 2002