Machine Vision and Applications

, Volume 21, Issue 4, pp 555–576 | Cite as

An adaptive, real-time, traffic monitoring system

  • Tomás RodríguezEmail author
  • Narciso García
Original Paper


In this paper we describe a computer vision-based traffic monitoring system able to detect individual vehicles in real-time. Our fully integrated system first obtains the main traffic variables: counting, speed and category; and then computes a complete set of statistical variables. The objective is to investigate some of the difficulties impeding existing traffic systems to achieve balanced accuracy in every condition; i.e. day and night transitions, shadows, heavy vehicles, occlusions, slow traffic and congestions. The system we present is autonomous, works for long periods of time without human intervention and adapts automatically to the changing environmental conditions. Several innovations, designed to deal with the above circumstances, are proposed in the paper: an integrated calibration and image rectification step, differentiated methods for day and night, an adaptive segmentation algorithm, a multistage shadow detection method and special considerations for heavy vehicle identification and treatment of slow traffic. A specific methodology has been developed to benchmark the accuracy of the different methods proposed.


Input Image Control Area Heavy Vehicle Bright Object Vehicle Category 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Huang M.-C., Yen S.-H.: A real-time and color-based computer vision for traffic monitoring system. IEEE Int. Conf. Multimedia Expo 3, 2119–2122 (2004)Google Scholar
  2. 2.
    Koller, D., Weber, J., Malik, J.: Robust multiple car tracking with occlusion reasoning, In: European Conference on Computer Vision, pp. 189–196 (1994)Google Scholar
  3. 3.
    Chen, T.-H., Lin, Y.-F., Chen, T.-Y.: Intelligent vehicle counting method based on blob analysis in traffic surveillance. In: Proceedings of the Second International Conference on Innovative Computing, Information and Control, vol. 0, pp. 238–242 (2007)Google Scholar
  4. 4.
    MNDOT, S.C. Group: Evaluation of non-intrusive technologies for traffic detection, final report, September 2002, Federal Highway Administration, US Department of Transportation, Technical Report (2002)Google Scholar
  5. 5.
    Middleton, D., Parker, R.: Initial evaluation of selected detectors to replace inductive loops on freeways, Texas Transportation Institute, Technical Report FHWA/TX1439-7, April 2003Google Scholar
  6. 6.
    Sensor development final report, traffic surveillance and detection technology development. Jet Propulsion Laboratory, Pasadena, California, Technical Report FHWA-RD-77-86, March 1997Google Scholar
  7. 7.
    Michalopoulos P.G.: Vehicle detection through image processing: the autoscope system 40(1), 21–29 (1991)Google Scholar
  8. 8.
    Panda, D.: An integrated video sensor design for traffic management and control. In: IMACS IEEE CSCC 99 International Multi-conference, Athens, Greece, July 1999, pp. 176–185 (1999)Google Scholar
  9. 9.
    Rodríguez, T.: Diseño e implementación de un sistema de visión artificial para la medida de variables de tráfico. Ph.D. dissertation, May (2007)Google Scholar
  10. 10.
    Cucchiara, R., Grana, C., Prati, A.: Detecting moving objects and their shadows: an evaluation with the pets 2002 dataset. In: Proceedings of Third IEEE International Workshop on Performance Evaluation of Tracking and Surveillance. ECCV 2002. Copenhagen, Denmark, May 2002, pp. 18–25 (2002)Google Scholar
  11. 11.
    Foresti G., Micheloni C., Snidaro L.: Advanced visual-based traffic monitoring systems for increasing safety in road transportation. Adv. Transp. Stud. 1, 22–47 (2003)Google Scholar
  12. 12.
    Zang, Q., Klette, R.: Object classification and tracking in video surveillance. In: Proceedings of the CAIP, pp. 198–205 (2003)Google Scholar
  13. 13.
    Lai A., Yung N.H.C.: Lane detection by orientation and length discrimination. IEEE Trans. Syst. Man Cybern. 30(4), 539–548 (2000)CrossRefGoogle Scholar
  14. 14.
    Xu, G., Zhang, Z.: Epipolar Geometry in Stereo, Motion and Object Recognition, Chap. 3. Kluwer, Dordrecht (1996)Google Scholar
  15. 15.
    Rodríguez T.: Practical camera calibration and image rectification in monocular road traffic applications. Mach. Graphics Vis. J. 15(1), 51–71 (2006)Google Scholar
  16. 16.
    Rodríguez T.: Camera calibration and image rectification in a traffic monitoring system. Adv. Transp. Stud. B(8), 81–96 (2006)Google Scholar
  17. 17.
    Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: Proceedings of the CVPR, vol. 2, pp. 142–149 (2000)Google Scholar
  18. 18.
    Melo, J., Naftel, A., Bernardino, A., Santos-Victor, J.: Viewpoint independent detection of vehicle trajectories and lane geometry from uncalibrated traffic surveillance cameras. In: Proceedings of the ICIAR (2), pp. 454–462 (2004)Google Scholar
  19. 19.
    Owens, J., Hunterb, A., Fletcher, E.: A fast model-free morphology-based object tracking algorithm. In: 13th British Machine Vision Conference BMVC 2002, Cardiff, UK, September 2002, pp. 767–776 (2002)Google Scholar
  20. 20.
    Huang M.C., Yen S.H.: A real-time and color-based computer vision for traffic monitoring system. IEEE Int. Conf. Multimedia Expo 3, 2119–2122 (2004)Google Scholar
  21. 21.
    Iera, A., Modafferi, A., Musolino, G., Vitetta, A.: An experimental station for real-time traffic monitoring on a urban road. In: Proceedings of the 5th IEEE International Conference on Intelligent Transportation Systems, pp. 697–770 (2002)Google Scholar
  22. 22.
    Faugeras O.: Three Dimensional Computer Vision, A Geometric Viewpoint. MIT Press, Cambridge (1993)Google Scholar
  23. 23.
    Cucchiara R., Piccardi M., Mello P.: Image analysis and rule-based reasoning for a traffic monitoring system. IEEE Trans. Intell. Transp. Syst. 1(2), 119–130 (2000)CrossRefGoogle Scholar
  24. 24.
    Nadimi S., Bhanu B.: Physical models for moving shadow and object detection in video. 26(8), 1079–1087 (2004)Google Scholar
  25. 25.
    Seki, M., Fujiwara, H., Sumi, K.: A robust background subtraction method for changing background. In: Proceedings of IEEE Workshop on Applications of Computer Vision, pp. 207–213 (2000)Google Scholar
  26. 26.
    Ohta, N.: A statistical approach to background suppression for surveillance systems. In: Proceedings of IEEE International Conference on Computer Vision, pp. 481–486 (2001)Google Scholar
  27. 27.
    Rodríguez T.: Adaptive real-time segmentation in traffic sequences. Mach. Graphics Vis. J. 13(1), 39–52 (2004)Google Scholar
  28. 28.
    Ridder, C., Munkelt, O., Kirchner, H.: Adaptive background estimation and foreground detection using kalman-filtering. In: Proceedings of the ICRAM 95193-195. UNESCO (1995)Google Scholar
  29. 29.
    Cucchiara, R., Grana, C., Piccardi, M., Prati, A.: Statistical and knowledge based moving object detection in traffic scenes. In: Proceedings of the IEEE International Conference on Intelligent Transportation Systems. October 2000, pp. 27–32 (2000)Google Scholar
  30. 30.
    Carlo, Kanade, T.: Detection and tracking of point features. No. CMU-CS-91-132, April (1991)Google Scholar
  31. 31.
    Tai J., Tseng S., Lin C., Song K.: Real-time image tracking for automatic traffic monitoring and enforcement applications. 22(6), 485–501 (2004)Google Scholar
  32. 32.
    Yilmaz A., Javed O., Shah M.: Object tracking: a survey. ACM J. Comput. Surv. 38(4), 13.1–13.45 (2006)Google Scholar
  33. 33.
    Sheikh Y., Shah M.: Bayesian modeling of dynamic scenes for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 27(11), 1778–1792 (2006)CrossRefGoogle Scholar
  34. 34.
    Yoneyama, A., Yeh, C., Kuo, C.: Moving cast shadows elimination for robust vehicle extraction based on 2d joint/vehicle shadow models. In: IEEE Conference on Advanced Video and Signal Based Surveillance. AVSS03, pp. 229–236 (2003)Google Scholar
  35. 35.
    Martin, P.T.: Detector technology evaluation. University of Utah, Department of Civil and Environmental Engineering, Technical Report CMU-CS-91-132, April (2003)Google Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  1. 1.ETSI InformáticaUniversidad Nacional de Educación a DistanciaMadridSpain
  2. 2.Grupo de Tratamiento de ImágenesUniversidad Politécnica de MadridMadridSpain

Personalised recommendations