Skip to main content
SpringerLink
Log in

Pedestrian tracking using color, thermal and location cue measurements: a DSmT-based framework

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

In this paper, we address the problem of pedestrians tracking in cluttered scenes using location, color and thermal cues. The Dezert–Smarandache (DSm) theoretical framework is used to combine the measurements provided by the sensors into a single and unified frame. The use of DSm Theory allows modeling the conflicts that might arise between the sensors due to the presence of clutter and high levels of occlusion. The location cue is integrated as a prior knowledge, which increases the robustness of the tracking. A belief measure is derived and used as a step in a particle filtering algorithm. Finally, experimental results are given, where the developed approach is used to track walking persons in indoor scenes with high levels of occlusion and clutter.

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

  1. Stringa E., Regazzoni C.S.: Real-time video shot detection for scene surveillance applications. IEEE Trans. Image Proc. 9, 69–79 (2000)

    Article  Google Scholar 

  2. Czyz J., Ristic B., Macq B.: A particle filter for joint detection and tracking of color objects. Image Vis. Comput. 25(8), 1271–1281 (2007)

    Article  Google Scholar 

  3. Yang Y., Teoh E.K., Shen D.: Lane detection and tracking using B-Snake. Image Vis. Comput. 22(4), 269–280 (2004)

    Article  Google Scholar 

  4. Schultz, D., Burgard, W., Fox, D., Cremers, A.B.: Tracking multiple moving targets with a mobile robot using particle filters and statistical data association. In: Proceedings of IEEE International Conference on Robotics and Automation, vol. 1, pp. 1665–1670 (2001)

  5. Chen C., Lin X., Shi Y.: Moving object tracking under varying illumination conditions. Pat. Rec. Let. 27(14), 1632–1643 (2006)

    Article  Google Scholar 

  6. Frank, O., Nieto, J., Guivant, J., Scheding, S.: Multiple target tracking using sequential Monte Carlo methods and statistical data association. In: Proceedings of the IEEE International Conference on Intelligence Robotics and Systems, vol. 3, pp. 2718–2723 (2003)

  7. Ozyilidiz E., Krahnstover N., Sharma R.: Adaptive texture and color segmentation for tracking moving objects. Pat. Rec. 35(10), 2013–2029 (2002)

    Article  Google Scholar 

  8. McCane B., Galvin B., Novins K.: Algorithmic fusion for more robust feature tracking. Int. J. Comput. Vis. 49(1), 79–89 (2002)

    Article  MATH  Google Scholar 

  9. Dewasurendra D.A., Bauer P.H., Premaratne K.: Evidence filtering. IEEE Trans. Signal Proc. 55(12), 5796–5805 (2007)

    Article  MathSciNet  Google Scholar 

  10. Ramasso E., Panagiotakis C., Pellerin D., Rombaut M.: Human action recognition in videos based on the transferable belief model: application to atheletics jumps. Pat. Anal. App. 11(1), 1–19 (2008)

    Article  MathSciNet  Google Scholar 

  11. Ramasso, E., Rombaut, M., Pellerin, D.: Forward–backword viterbi procedures in the transferable belief model for state sequence analysis using belief functions. Lecture Notes in Artificial Intelligence, vol. 4724, pp. 405–417 (2007)

  12. Shafer G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)

    MATH  Google Scholar 

  13. Shafer G.: Perspectives on the theory and practice of belief functions. Int. J. Approx. Reason. 4, 323–362 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  14. Zadeh L.: A simple view of the Dempster–Shafer theory of evidence and its implication for the rule of combination. AI Magazine. 7(2), 85–90 (1986)

    Google Scholar 

  15. Smarandache F., Dezert J.: Advances and applications of DSmT for information Fusion. Collected works, vol. 3, Am. Res. Press (ARP) (2009)

  16. Leung, Li Y., Bosse E., Blanchette M., Chan K.C.C.: Improved multiple target tracking using Dempster–Shafer identification, signal processing, sensor fusion, and target recognition, Orlando, pp. 218–227, 20–25 April 1997

  17. Klein J., Lecomte C., Miché P.: Hierarchical and conditional combination of belief functions induced by visual tracking. Int. J. Approx. Reason. 51(4), 410–428 (2010)

    Article  Google Scholar 

  18. Dezert J., Tchamova A., Smarandache F., Konstantinnova P.: Target type tracking with PCR5 and Dempster’s rules: a comparative analysis. In: Proceedings of Fusion 2006 International Conference on Information Fusion, Fusion 2006, Firenze, Italy, 10–13 July 2006

  19. Sun Y., Bentabet L.: A particle filtering and DSmT based approach for conflict resolving in case of target tracking with multiple cues. J. Math. Imag. Vis. 36(2), 159–167 (2010)

    Article  MathSciNet  Google Scholar 

  20. Cielniak G., Duckett T., Lilienthal A.J.: Data association and occlusion handling for vision-based people tracking by mobile robots. Robot. Auton. Syst. 58(5), 435–443 (2010)

    Article  Google Scholar 

  21. Arulampalam M.S., Maskell S., Gordon N., Clapp T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Process. 50(2), 174–188 (2002)

    Article  Google Scholar 

  22. Hue C., Le Cadre J.-P., Pérez P.: Sequential Monte Carlo methods for multiple target tracking and data fusion. IEEE Trans. Signal Process. 50(2), 309–325 (2002)

    Article  Google Scholar 

  23. Hue C., Le Cadre J.-P., Pérez P.: Tracking multiple objects with particle filtering. IEEE Trans. Aerosp. Electron. Syst. 38(3), 791–812 (2002)

    Article  Google Scholar 

  24. Gustafsson F., Gunnarsson F., Bergman N., Forssell U., Jansson J., Karlsson R., Nordlund P.-J.: Particle filters for positioning, navigation and tracking. IEEE Trans. Signal Process. 50(2), 425–437 (2002)

    Article  Google Scholar 

  25. Avitzour D.: Stochastic Simulation Bayesian Approach to Multitarget Approach. Proc. Inst. Elect. Eng. Radar, Sonar (1995)

    Google Scholar 

  26. Isard M., Blake A.: Condensation—conditional density propagation for visual tracking. Int. J. Comput. Vis. 29(1), 5–28 (1998)

    Article  Google Scholar 

  27. Reid D.: An algorithm for tracking multiple targets. IEEE Trans. Automat. Control 24(AC-6), 84–90 (1979)

    MathSciNet  Google Scholar 

  28. Gauvrit H., Le Cadre J.-P., Jauffret C.: A formulation of multitarget tracking as an incomplete data problem. IEEE Trans. Aerosp. Electron. Syst. 33, 1242–1257 (1997)

    Article  Google Scholar 

  29. Ristic B., Arulampalam S., Gordon N.: Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House, Massachusetts (2004)

    MATH  Google Scholar 

  30. Bar-Shalom Y., Dale Blair W.: Multitarget-Multisensor Tracking: Application and Advances, vol. III. Artech House, Massachusetts (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Applied Automation Laboratory, Boumerdes University, Boumerdes, Algeria

    M. Airouche & M. Zelmat

  2. Computer Science Department, Bishop’s University, Sherbrooke, Canada

    M. Airouche, L. Bentabet & G. Gao

Authors
  1. M. Airouche
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. L. Bentabet
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Zelmat
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. G. Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Airouche.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Airouche, M., Bentabet, L., Zelmat, M. et al. Pedestrian tracking using color, thermal and location cue measurements: a DSmT-based framework. Machine Vision and Applications 23, 999–1010 (2012). https://doi.org/10.1007/s00138-011-0342-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-011-0342-z

Keywords

Search

Navigation