Skip to main content
SpringerLink
Log in

Visual Surveillance for Moving Vehicles

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

Abstract

An overview is given of a vision system for locating, recognising and tracking multiple vehicles, using an image sequence taken by a single camera mounted on a moving vehicle. The camera motion is estimated by matching features on the ground plane from one image to the next. Vehicle detection and hypothesis generation are performed using template correlation and a 3D wire frame model of the vehicle is fitted to the image. Once detected and identified, vehicles are tracked using dynamic filtering. A separate batch mode filter obtains the 3D trajectories of nearby vehicles over an extended time. Results are shown for a motorway image sequence.

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

  • Bertozzi, M. and Broggi, A. 1996. Real-time lane and obstacle detection on the GOLD system. In Proceedings IEEE Intelligent Vehicles Symposium, pp. 213–218.

  • Betke, M., Haritaoglu, E., and Davis, L. 1996. Multiple vehicle detection and tracking in hard real-time. In Proceedings IEEE Intelligent Vehicles Symposium, pp. 351–356.

  • Beymer, D. and Malik, J. 1996. Tracking vehicles in congested traffic. In Proceedings IEEE Intelligent Vehicles Symposium, pp. 130–135.

  • Dellaert, F., Pomerleau, D., and Thorpe, C. 1998. Model-based car tracking integrated with a road follower. In Proceedings International Conference on Robotics and Automation, ICRA'98, pp. 1189–1194.

  • Dickmanns, E. and Mysliwetz, B. 1992. Recursive 3D road and relative ego-state recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2):199–213.

    Google Scholar 

  • Dickmanns, E.D. 1997. Vehicles capable of dynamic vision. In Proceedings International Joint Conference on Artificial Intelligence, IJCAI, Nagoya, Japan, pp. 1577–1592.

  • Ferryman, J.M., Worrall, A.D., Sullivan, G.D., and Baker, K.D. 1995. Visual surveillance using deformable models of vehicles. In Proceedings International Symposium on Intelligent Robotic Systems, Pisa, Italy, pp. 295–302.

  • Fischler, M.A. and Bolles, R.C. 1981. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications ACM, 24:381–395.

    Google Scholar 

  • Gennery, D. 1992. Visual tracking of known three-dimensional objects. International Journal of Computer Vision, 7(3):243–270.

    Google Scholar 

  • Giachetti, A., Campani, M., and Torre, V. 1994. The use of optical flow for autonomous navigation. In Proceedings 3rd European Conference on Computer Vision, pp. 146–151.

  • Harris, C. 1992. Tracking with rigid models. In Active Vision, A. Blake and A. Yuille (Eds.). MIT Press: Cambridge, USA.

    Google Scholar 

  • Harris, C. and Stephens, M. 1988. A Combined corner and edge detector. In Proceedings Alvey Vision Conference, University of Manchester, pp. 147–151.

  • Kanatani, K. 1993. Geometric Computation for Machine Vision. Oxford Science Publications. Oxford Engineering Science Series No. 37.

  • Koller, D., Weber, J., and Malik, J. 1994. Towards realtime visual based tracking in cluttered traffic scenes. In Proceedings IEEE Intelligent Vehicles Symposium, Press W.H. Ten pp. 201–206.

  • Luong, Q.-T., Weber, J., Koller, D., and Malik, J. 1995. An integrated stereo-based approach to automatic vehicle guidance. In Proceedings International Conference on Computer Vision, ICCV'95, Boston, pp. 12–20.

  • Matthews, N.D., An, P.E., and Harris, C.J. 1995. Vehicle detection and recognition for autonomous intelligent cruise control. Technical Report, Department of Electronics and Computer Science, University of Southampton, UK.

    Google Scholar 

  • Maybank, S.J., Worrall, A.D., and Sullivan, G.D. 1996. Filter for car tracking based on acceleration and steering angle. In Proceedings British Machine Vision Conference 1996, Vol. 2, R.B. Fisher and E. Trucco (Eds.). BMVA, pp. 615–624.

  • Press, W.H., Ten Kolsky, S.A., Vetterling, W.T., and Flannery, B.P. 1986. Numerical Recipes. Cambridge University Press.

  • Schmid, M. 1994. An approach to model-based 3D recognition of vehicles in real time by machine vision. In Proceedings International Conference on Intelligent Robots and Systems, IROS'94, 3, Munich, Germany.

  • Smith, S.M. 1995. ASSET-2: Real-time motion segmentation and shape tracking. In Proceedings International Conference on Computer Vision, pp. 237–244.

  • Sullivan, G.D. 1994. Visual interpretation of known objects in constrained scenes, Phil. Trans. R.Soc. Lon., B, 337, pp. 361–370.

    Google Scholar 

  • Sullivan, G.D. 1994. Model-based vision for traffic scenes using the ground-plane constraint. In Real-time Computer Vision, D. Terzopoulos and C. Brown (Eds.). Cambridge University Press.

  • Tan, T.N., Sullivan, G.D., and Baker, K.D. 1996. Efficient gradient-based object localisation and recognition. In Proceedings International Conference on Computer Vision and Pattern Recognition, San Francisco, pp. 397–402.

  • Thomanek, F. and Dickmanns, D. 1992. Obstacle Detection, Tracking and state estimation for autonomous road vehicle guidance. In Proceedings International Conference on Robotics and Automation, ICRA'92, Raleigh, NC, p. 1399.

  • Tian, T.Y., Tomasi, C., and Heeger, D.J. 1996. Comparison of approaches to egomotion computation. In Proceedings International Conference on Computer Vision and Pattern Recognition, San Francisco, pp. 315–320.

  • Weber, J. 1995. Further results on the use of binocular vision for highway driving, transportation sensors and controls: Collision avoidance, traffic management and ITS. In Proceedings International Society for Optical Engineering. SPIE, Vol. 2595.

  • Wolfram, S. 1996. The Mathematica Book, 3rd ed. Wolfram Media/Cambridge University Press.

  • Worrall, A.D., Baker, K.D., and Sullivan, G.D. 1989. Model-based perspective inversion, Image and Vision Computing Journal, 7(1):17–23, 1989.

    Google Scholar 

  • Worrall, A.D., Sullivan, G.D., and Baker, K.D. 1993. Advances in model-based traffic vision. In Proceedings 4th British Machine Vision Conference. BMVA Press, pp. 559–568.

  • Worrall, A.D., Sullivan, G.D., and Baker, K.D. 1994. A simple, intuitive camera calibration tool for natural images. In Proceedings 5th British Machine Vision Conference, pp. 781–790.

  • Wu, J.J., Rink, R.E., Caelli, T.M., and Gourishamkar, V.G. 1989. Recovery of the3Dlocation and motion of a rigid object through camera image (An extended Kalman filter approach). International Journal of Computer Vision, 2:373–394.

    Google Scholar 

  • Zhao, L. and Thorpe, C. 1998. Qualitative and quantitative car tracking from a range image. In Proceedings International Conference on Computer Vision and Pattern Recognition, pp. 496–501.

Download references

Author information

Authors and Affiliations

  1. Computational Vision Group, Department of Computer Science, The University of Reading, Berkshire, RG6 6AY, England, UK

    James M. Ferryman, Stephen J. Maybank & Anthony D. Worrall

Authors
  1. James M. Ferryman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stephen J. Maybank
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anthony D. Worrall
    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

Ferryman, J.M., Maybank, S.J. & Worrall, A.D. Visual Surveillance for Moving Vehicles. International Journal of Computer Vision 37, 187–197 (2000). https://doi.org/10.1023/A:1008155721192

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1008155721192

Search

Navigation