Machine Vision and Applications

, Volume 21, Issue 3, pp 275–286 | Cite as

Tracking of vehicle trajectory by combining a camera and a laser rangefinder

  • Y. Goyat
  • T. ChateauEmail author
  • L. Trassoudaine
Original Paper


This article presents a probabilistic method for vehicle tracking using a sensor composed of both a camera and a laser rangefinder. Two main contributions will be set forth in this paper. The first involves the definition of an original likelihood function based on the projection of simplified 3D vehicle models. We will also propose an efficient approach to compute this function using a line-based integral image. The second contribution focuses on a sampling algorithm designed to handle several sources. The resulting modified particle filter is capable of naturally merging several observation functions in a straightforward manner. Many trajectories of a vehicle equipped with a kinematic GPS1 have been measured on actual field sites, with a video system specially developed for the project. This field input has made it possible to experimentally validate the result obtained from the algorithm. The ultimate goal of this research is to derive a better understanding of driver behavior in order to assist road managers in their effort to ensure network safety.


Visual tracking Particle filter Sensor fusion 


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  1. 1.
    Arulampalam, S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for on-line non-linear/non-gaussian bayesian tracking. IEEE Trans. Signal Process. 50(2),174–188 (2002). URL:
  2. 2.
    Avidan, S.: Support vector tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’2001), Hawaii (2001)Google Scholar
  3. 3.
    Chateau, T., Gay-Belille, V., Chausse, F., Lapresté, J.: Real-time tracking with classifiers. In: WDV 2006—WDV Workshop on Dynamical Vision at ECCV2006, Grazz (2006)Google Scholar
  4. 4.
    Duda R.O., Hart P.E., Stork D.G.: Pattern Classification, 2nd edn. Wiley-Interscience, New York (2001)zbMATHGoogle Scholar
  5. 5.
    Fleuret F., Berclaz J., Lengagne R., Fua P.: Multi-camera people tracking with a probabilistic occupancy map. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 267–282 (2008)CrossRefGoogle Scholar
  6. 6.
    Gillespie T.: Fundamentals of Vehicle Dynamics. Society of Automotive Engineers (SAE), USA (1992)Google Scholar
  7. 7.
    Goyat, Y., Chateau, T., Malaterre, L., Trassoudaine, L.: Vehicle trajectories evaluation by static video sensors. In: ITSC06 2006—9th International IEEE Conference on Intelligent Transportation Systems (2006)Google Scholar
  8. 8.
    Haag M., Nagel H.: Combination of edge element and optical flow estimate for 3d-model-based vehicle tracking in traffic image sequences. Int. J. Comp. Vis. 35(9), 295–319 (1999)CrossRefGoogle Scholar
  9. 9.
    Isard M., MacCormick J.: Bramble: A bayesian multiple-blob tracker. Int. Conf. Comp. Vis. 2, 34–41 (2001)Google Scholar
  10. 10.
    Ivanov Y., Bobick A., Liu J.: Fast Lighting Independant Background Subtraction. MIT Media Laboratory, Cambridge (1997)Google Scholar
  11. 11.
    Kamijo, S., Ikeuchi, K., Sakauchi, M.: Vehicle tracking in low-angle and front view images based on spatio-temporal markov random fields. In: 8th World Congress on Intelligent Transportation Systems (ITS) (2001)Google Scholar
  12. 12.
    Kanhere, N.K., Pundlik, S.J., Birchfield, S.T.: Vehicle segmentation and tracking from a low-angle off-axis camera. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, USA (2005)Google Scholar
  13. 13.
    Khan Z., Balch T., Dellaert F.: Mcmc-based particle filtering for tracking a variable number of interacting targets. IEEE Trans. Pattern Anal. Mach. Intell. 27(11), 1805–1918 (2005)CrossRefGoogle Scholar
  14. 14.
    Kim, Z.W., Malik, J.: Fast vehicle detection with probabilistic feature grouping and its application to vehicle tracking. In: International Conference on Computer Vision (ICCV), pp. 521–528 (2003)Google Scholar
  15. 15.
    Koller D., Dandilis K., Nagel H.H.: Model based object tracking in monocular image sequences of road traffic scenes. Int. J. Comp. Vis. 10(3), 257–281 (1993)CrossRefGoogle Scholar
  16. 16.
    Isard M., Blake A.: Condensation—conditional density propagation for visual tracking. IJCV: Int. J. Comp. Vis. 29(1), 5–28 (1998)CrossRefGoogle Scholar
  17. 17.
    Magee D.R.: Tracking multiple vehicles using foreground, background and motion models. Image Vis. Comput. 22(2), 143–155 (2004)CrossRefMathSciNetGoogle Scholar
  18. 18.
    Mikik, I., Cosman, P., Kogut, G., Trivedi, M.: Moving shadow and object detection in traffic scenes. In: International Conference on Pattern Recognition, Barcelona, Spain (2000)Google Scholar
  19. 19.
    Pece, A., Worrall, A.: Tracking with the em contour algorithm. In: ECCV European Conference on Computer Vision, vol. 1, pp. 3–17, Copenhagen (2002)Google Scholar
  20. 20.
    Salvador, E., Cavallaro, A., Ebrahimi, T.: Cast shadow segmentation using invariant color features. Comput. Vis. Image Underst. 95(2), 238–259 (2004). doi: 10.1016/j.cviu.2004.03.008 CrossRefGoogle Scholar
  21. 21.
    Schneiderman, H., Kanade, T.: A statistical model for 3d object detection applied to faces and cars. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (2000)Google Scholar
  22. 22.
    Sheikh Y., Shah M.: Bayesian modeling of dynamic scenes for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 27(11), 1778–1792 (2005)CrossRefGoogle Scholar
  23. 23.
    Smith, K., Gatica-Perez, Odobez, J.: Using particles to track varying numbers of interacting people. In: International Conference on Computer Vision and Pattern Recognition (CVPR) (2005)Google Scholar
  24. 24.
    Stauffer C., Grimson W.E.L.: Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 747–757 (2000)CrossRefGoogle Scholar
  25. 25.
    Tan T.N., Baker K.D.: Efficient image gradient based vehicle localization. IEEE Trans. Image Process. 9(8), 1343–1356 (2000)CrossRefGoogle Scholar
  26. 26.
    Yu, Q., Medioni, G., Cohen, I.: Multiple target tracking using spatio-temporal markov chain monte carlo data association. In: International Conference on Computer Vision and Pattern Recognition (CVPR) (2007)Google Scholar
  27. 27.
    Zhao, T., Nevatia, R.: Car detection in low resolution aerial image. In: ICCV, pp. 710—717 (2001)Google Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.LCPCBouguenaisFrance
  2. 2.LASMEAAubière CedexFrance

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