Multi-class Vehicle Type Recognition System

  • Xavier Clady
  • Pablo Negri
  • Maurice Milgram
  • Raphael Poulenard
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5064)

Abstract

This paper presents a framework for multiclass vehicle type (Make and Model) identification based on oriented contour points. A method to construct a model from several frontal vehicle images is presented. Employing this model, three voting algorithms and a distance error allows to measure the similarity between an input instance and the data bases classes. These scores could be combined to design a discriminant function. We present too a second classification stage that employ scores like vectors. A nearest-neighbor algorithm is used to determine the vehicle type. This method have been tested on a realistic data set (830 images containing 50 different vehicle classes) obtaining similar results for equivalent recognition frameworks with different features selections [12]. The system also shows to be robust to partial occlusions.

References

  1. 1.
    Cootes, T., Taylor, C.: On representing edge structure for model matching. In: Conference on Vision and Pattern Recognition, Hawai, USA, December 2001, vol. 1, pp. 1114–1119 (2001)Google Scholar
  2. 2.
    Douret, J., Benosman, R.: A multi-cameras 3d volumetric method for outdoor scenes: a road traffic monitoring application. In: International Conference on Pattern Recognition, pp. III: 334–337 (2004)Google Scholar
  3. 3.
    Dlagnekov, L.: Video-based car surveillance: License plate make and model recognition. Masters Thesis, University of California at San Diego (2005)Google Scholar
  4. 4.
    Dubuisson, M., Jain, A.: A modified hausdorff distance for object matching. In: International Conference on Pattern Recognition, vol. A, pp. 566–569 (1994)Google Scholar
  5. 5.
    Dubuisson-Jolly, M., Lakshmanan, S., Jain, A.: Vehicle segmentation and classification using deformable templates. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(3), 293–308 (1996)CrossRefGoogle Scholar
  6. 6.
    Ferryman, J.M., Worrall, A.D., Sullivan, G.D., Baker, K.D.: A generic deformable model for vehicle recognition. In: British Machine Vision Conference, pp. 127–136 (1995)Google Scholar
  7. 7.
    Han, D., Leotta, M.J., Cooper, D.B., Mundy, J.L.: Vehicle class recognition from video-based on 3d curve probes. In: VS-PETS, pp. 285–292 (2005)Google Scholar
  8. 8.
    Hond, D., Spacek, L.: Distinctive descriptions for face processing. In: British Machine Vision Conference, University of Essex, UK (1997)Google Scholar
  9. 9.
    Kazemi, F.M., Samadi, S., Pooreza, H.R., Akbarzadeh-T, M.R.: Vehicle Recognition Based on Fourier, Wavelets and Curvelet Transforms - a Comparative Study. In: IEEE International conference on Information Technology, ITNG 2007 (2007)Google Scholar
  10. 10.
    Lai, A.H.S., Fung, G.S.K., Yung, N.H.C.: Vehicle type classification from visual-based dimension estimation. In: IEEE International System Conference, pp. 201–206 (2001)Google Scholar
  11. 11.
    Olson, C.F., Huttenlocher, D.P.: Automatic target recognition by matching oriented edge pixels. IEEE Transactions on Image Processing 6(1), 103–113 (1997)CrossRefGoogle Scholar
  12. 12.
    Petrovic, V.S., Cootes, T.F.: Analysis of features for rigid structure vehicle type recognition. In: British Machine Vision Conference, vol. 2, pp. 587–596 (2004)Google Scholar
  13. 13.
    Petrovic, V.S., Cootes, T.F.: Vehicle Type Recognition with Match Refinement. In: International Conference on Pattern Recogntion, vol. 3, pp. 95–98 (2004)Google Scholar
  14. 14.
    Munroe, D.T., Madden, M.G.: Multi-Class and Single-Class Classification Approaches to Vehicle Model Recognition from Images. In: AICS (2005)Google Scholar
  15. 15.
    Sun, Z., Bebis, G., Miller, R.: On-road vehicle detection: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(5), 694–711 (2006)CrossRefGoogle Scholar
  16. 16.
    Torres, D.A.: More Local Structure for Make-Model Recognition (2007)Google Scholar
  17. 17.
    Zafar, I., Acar, B.S., Edirisinghe, E.A.: Vehicle Make and Model Identification using Scale Invariant Transforms. In: IASTED (2007)Google Scholar
  18. 18.
    Zafar, I., Edirisinghe, E.A., Acar, B.S., Bez, H.E.: Two Dimensional Statistical Linear Discriminant Analysis for Real-Time Robust Vehicle Type Recognition. In: SPIE, vol. 6496 (2007)Google Scholar
  19. 19.
    Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A literature survey. ACM Computing Surveys 35(4), 399–458 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Xavier Clady
    • 1
  • Pablo Negri
    • 1
  • Maurice Milgram
    • 1
  • Raphael Poulenard
    • 2
  1. 1.Institut des Systèmes Intelligents et RobotiqueUniversité Pierre et Marie Curie-Paris 6, CNRS FRE 2907 
  2. 2.LPR Editor - Montpellier 

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