Skip to main content
SpringerLink
Log in

Model-based object tracking in monocular image sequences of road traffic scenes

  • Published:
International Journal of Computer 11263on Aims and scope Submit manuscript

Abstract

Moving vehicles are detected and tracked automatically in monocular image sequences from road traffic scenes recorded by a stationary camera. In order to exploit the a priori knowledge about shape and motion of vehicles in traffic scenes, a parameterized vehicle model is used for an intraframe matching process and a recursive estimator based on a motion model is used for motion estimation. An interpretation cycle supports the intraframe matching process with a state MAP-update step. Initial model hypotheses are generated using an image segmentation component which clusters coherently moving image features into candidate representations of images of a moving vehicle. The inclusion of an illumination model allows taking shadow edges of the vehicle into account during the matching process. Only such an elaborate combination of various techniques has enabled us to track vehicles under complex illumination conditions and over long (over 400 frames) monocular image sequences. Results on various real-world road traffic scenes are presented and open problems as well as future work are outlined.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Bar-Shalom, Y., and Fortmann, T.E., 1988.Tracking and Data Association. Academic Press: New York.

    MATH  Google Scholar 

  • Broida, T.J., Chandrashekhar, S., and Chellappa, R., 1990. Recursive 3-d motion estimation from a monocular image sequence,IEEE Trans. Aerospace Electron. Syst. 26: 639–656.

    Article  Google Scholar 

  • Deriche, R., and Faugeras, O.D., 1990. Tracking line segments,Image Vis. Comput. 8: 261–270.

    Article  Google Scholar 

  • Evans, R., 1990. Kalman filtering of pose estimates in applications of the rapid video rate tracker,Proc. Brit. Mach. Vis. Conf., Oxford, pp. 79–84, September 24–27.

  • Gelb, A., ed., 1974.Applied Optimal Estimation. MIT Press: Cambridge, MA and London.

    Google Scholar 

  • Gennery, D.B., 1982. Tracking known three-dimensional objects,Proc. Conf. Amer. Assoc. Artif. Intell., Pittsburgh, pp. 13–17, August 18–20.

  • Gennery, D.B., 1992. Visual tracking of known three-dimensional objects,Intern. J. Comput. Vis. 7: 243–270.

    Article  Google Scholar 

  • Grimson, W.E.L., 1990a. The combinatorics of object recognition in cluttered environments using constrained search,Artificial Intelligence 44: 121–165.

    Article  MathSciNet  MATH  Google Scholar 

  • Grimson, W.E.L., 1990b.Object Recognition by Computer: The Role of Geometric Constraints. MIT Press: Cambridge, MA.

    Google Scholar 

  • Harris, C., and Stennet, C., 1990. RAPID—a video rate object tracker,Proc. Brit. Mach. Vis. Conf., Oxford, pp. 73–77, September 24–27.

  • Jazwinski, A.H., 1970.Stochastic Processes and Filtering Theory. Academic Press: New York and London.

    MATH  Google Scholar 

  • Koller, D, 1992. Detektion, Verfolgung and Klassifikation bewegter Objekte in monokularen Bildfolgen am Beispiel von Straßenver-kehrsszenen. Dissertation, Fakultät für Informatik der Universität Karlsruhe (TH), available as vol. DISKI 13,Dissertationen zur Künstlichen Intelligenz, infix-Verlag, Sankt Augustin, Germany.

  • Koller, D., Heinze, N., and Nagel, H.-H., 1991. Algorithmic characterization of vehicle trajectories from image sequences by motion verbs,Conf. Comput. Vis. Patt. Recog., Lahaina, Maui, Hawaii, pp. 90–95, June 3–6.

  • Koller, D., Daniilidis, K., Thórhallson, T., and Nagel, H.-H., 1992. Model-based object tracking in traffic scenes,Proc. 2nd Europ. Conf. Comput. Vis., S. Margherita, Ligure, Italy, May 18–23. G. Sandini (ed.),Lecture Notes in Computer Science 588, Springer-Verlag: Berlin, Heidelberg, New York.

    Google Scholar 

  • Kollnig, H., 1992. Berechnung von Bewegungsverben und Ermittlung einfacher Abläufe. Diplomarbeit, Institut für Algorithmen und Kognitive Systeme, Fakultät für Informatik der Universität Karlsruhe (TH), Karlsruhe.

  • Korn, A.F., 1988. Towards a symbolic representation of intensity changes in images,IEEE Trans. Patt. Anal. Mach. Intell., 10: 610–625.

    Article  Google Scholar 

  • Lowe, D.G., 1987. Three-dimensional object recognition from single two-dimensional images,Artificial Intelligence 31: 355–395.

    Article  Google Scholar 

  • Lowe, D.G., 1990. Integrated treatment of matching and measurement errors for robust model-based motion tracking,Proc. 3rd Intern. Conf. Comput. Vis., Osaka, pp. 436–440, December 4–7.

  • Lowe, D.G., 1991. Fitting parameterized three-dimensional models to images.IEEE Trans. Patt. Anal. Mach. Intell. 13: 441–450.

    Article  Google Scholar 

  • Marslin, R.F., Sullivan, G.D., and Baker, K.D., 1991. Kalman filters in constrained model-based tracking,Proc. Brit. Mach. Vis. Conf., Glasgow, UK, pp. 371–374, September 24–26, Springer-Verlag, Berlin, Heidelberg, New York.

    Google Scholar 

  • Maybank, S., 1990. Filter-based estimates of depth,Proc. Brit. Mach. Vis. Conf., Oxford, pp. 349–354, September 24–27.

  • Mitschke, M., 1990.Dynamik der Kraftfahrzeuge: Band C—Fahrverhalten. Springer-Verlag: Berlin, Heidelberg, New York.

    Book  Google Scholar 

  • Murray, D.W., Castelow, D.A., and Buxton, B.F., 1989. From image sequences to recognized moving polyhedral objects,Intern. J. Comput. Vis. 3: 181–209.

    Article  Google Scholar 

  • Scales, L.E., 1985.Introduction to Non-Linear Optimization. Macmillan: London.

    Google Scholar 

  • Schick, J., and Dickmanns, E.D., 1991. Simultaneous estimation of 3D shape and motion of objects by computer 11263on,Proc. IEEE Workshop on Visual Motion, Princeton, NJ, pp. 256–261, October 7–9.

  • Sung, C.-K., 1988. Extraktion von typischen und komplexen Vorgängen aus einer Bildfolge einer Verkehrsszene. In H. Bunke, O. Kübler, and P. Stucki, (eds.),DAGM-Symposium Mustererkennung 1988, pp. 90–96, Zürich, Informatik-Fachberichte180, Springer-Verlag: Berlin, Heidelberg, New York.

    Chapter  Google Scholar 

  • Thompson, D.W., and Mundy, J.L., 1987. Model-based motion analysis—motion from motion. InRobotics Research, R. Bolles and B. Roth (eds.), MIT Press: Cambridge, MA, pp. 299–309.

    Google Scholar 

  • Thórhallson, T., 1991. Untersuchung zur dynamischen Modellan-passung in monokularen Bildfolgen. Diplomarbeit, Fakultät für Elektrotechnik der Universität Karlsruhe (TH), durchgeführt am Institut für Algorithmen und Kognitive Systeme, Fakultät für Informatik der Universität Karlsruhe (TH), Karlsruhe.

  • Tsai, R., 1987. A versatile camera calibration technique for high accuracy 3D machine 11263on metrology using off-the-shelf TV cameras and lenses,IEEE Trans. Robot. Autom. 3: 323–344.

    Article  Google Scholar 

  • Verghese, G., Gale, K.L., and Dyer, C.R., 1990. Real-time, parallel motion tracking of three dimensional objects from spatiotemporal images. In V. Kumar, P.S. Gopalakrishnan, and L.N. Kanal (eds.),Parallel Algorithms for Machine Intelligence and 11263on, pp. 340–359, Springer-Verlag: Berlin, Heidelberg, New York.

    Google Scholar 

  • Worrall, A.D., Marslin, R.F., Sullivan, G.D., and Baker, K.D., 1991. Model-based tracking,Proc. Brit. Mach. Vis. Conf., pp. 310–318, Glasgow, September 24–26, Springer-Verlag: Berlin, Heidelberg, New York.

    Google Scholar 

  • Wu, J.J., Rink, R.E., Caelli, T.M., and Gourishankar, V.G., 1988. Recovery of the 3-D location and motion of a rigid object through camera image (an extended Kalman filter approach),Intern. J. Comput. Vis. 3: 373–394.

    Google Scholar 

  • Young, G., and Chellappa, R., 1990. 3-D motion estimation using a sequence of noisy stereo images: models, estimation and uniqueness results,IEEE Trans. Patt. Anal. Mach. Intell. 12: 735–759.

    Article  Google Scholar 

  • Zhang, Z., and Faugeras, O.D., 1992. Three-dimensional motion computation and object segmentation in a long sequence of stereo frames,Intern. J. Comput. Vis. 7: 211–241.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institut für Algorithmen und Kognitive Systeme, Fakultät für Informatik der Universität Karlsruhe (TH), Postfach 6980, D-7500, Karlsruhe 1, Germany

    D. Koller, K. Daniilidis & H. H. Nagel

  2. Fraunhofer-Institut far Informations- und Datenverarbeitung (IITB), Karlsruhe

    H. H. Nagel

Authors
  1. D. Koller
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Daniilidis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. H. H. Nagel
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Koller, D., Daniilidis, K. & Nagel, H.H. Model-based object tracking in monocular image sequences of road traffic scenes. Int J Comput 11263on 10, 257–281 (1993). https://doi.org/10.1007/BF01539538

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01539538

Keywords

Search

Navigation