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The AIT Outdoors Tracking System for Pedestrians and Vehicles

  • Aristodemos Pnevmatikakis
  • Lazaros Polymenakos
  • Vasileios Mylonakis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4122)

Abstract

This paper presents the tracking system from Athens Information Technology that participated to the pedestrian and vehicle surveillance task of the CLEAR 2006 evaluations. Two are the novelties of the proposed tracker. First, we use a variation of Stauffer’s adaptive background algorithm with spatiotemporal adaptation of the learning parameters and a Kalman filter in a feedback configuration. In the feed-forward path, the adaptive background module provides target evidence to the Kalman filter. In the feedback path, the Kalman filter adapts the learning parameters of the adaptive background module. Second, we combine a temporal persistence pixel map, together with edge information, to produce the evidence that is associated with targets. The proposed tracker performed well in the evaluations, and can be also applied to indoors settings and multi-camera tracking.

Keywords

Kalman Filter Learning Rate Foreground Object Foreground Pixel Smart Space 
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.

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References

  1. 1.
    Interaction Loop. In: 5th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS), Lisbon, Portugal (Apr. 2004)Google Scholar
  2. 2.
    Pnevmatikakis, A., Talantzis, F., Soldatos, J., Polymenakos, L.: Robust Multimodal Audio-Visual Processing for Advanced Context Awareness in Smart Spaces. In: Maglogiannis, I., Karpouzis, K., Bramer, M. (eds.) Artificial Intelligence Applications and Innovations (AIAI06), pp. 290–301. Springer, Heidelberg (June 2006)CrossRefGoogle Scholar
  3. 3.
    Forsyth, D., Ponce, J.: Computer Vision - A Modern Approach, pp. 489–541. Prentice Hall, Englewood Cliffs (2002)Google Scholar
  4. 4.
    MacCormick, J.: Probabilistic modelling and stochastic algorithms for visual localisation and tracking, PhD Thesis, University of Oxford, section 4.6 (2000)Google Scholar
  5. 5.
    Jaffré, G., Crouzil, A.: Non-rigid object localization from color model using mean shift. In: International Conference on Image Processing (ICIP 2003), Barcelona, Spain (Sept. 2003)Google Scholar
  6. 6.
    Ekenel, H., Pnevmatikakis, A.: Video-Based Face Recognition Evaluation in the CHIL Project - Run 1. In: Face and Gesture Recognition, Southampton, UK, pp. 85–90 (March 2006)Google Scholar
  7. 7.
    McIvor, A.: Background Subtraction Techniques. In: Image and Vision Computing, New Zealand (2000)Google Scholar
  8. 8.
    Stauffer, C., Grimson, W.E.L.: Learning patterns of activity using real-time tracking. IEEE Trans. on Pattern Anal.and Machine Intel. 22(8), 747–757 (2000)CrossRefGoogle Scholar
  9. 9.
    KaewTraKulPong, P., Bowden, R.: An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection. In: Proc. 2nd European Workshop on Advanced Video Based Surveillance Systems (AVBS01) (Sept. 2001)Google Scholar
  10. 10.
    Landabaso, J.L., Pardas, M.: Foreground regions extraction and characterization towards real-time object tracking. In: Proceedings of Joint Workshop on Multimodal Interaction and Related Machine Learning Algorithms (MLMI’05) (2005)Google Scholar
  11. 11.
    Kalman, R.E.: A New Approach to Linear Filtering and Prediction Problems. Transactions of the ASME - Journal of Basic Engineering 82 (Series D), 35–45 (1960)Google Scholar
  12. 12.
    Xu, L.-Q., Landabaso, J.L., Pardas, M.: Shadow Removal with Blob-Based Morphological Reconstruction for Error Correction. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (March 2005)Google Scholar
  13. 13.
    Blackman, S.: Multiple-Target Tracking with Radar Applications. Artech House, Dedham (1986)Google Scholar
  14. 14.
    Zhang, Z.: A Flexible New Technique for Camera Calibration, Microsoft Research, Technical Report MSR-TR-98-71 (Aug. 2002)Google Scholar
  15. 15.
    Jones, M., Rehg, J.: Statistical color models with application to skin detection. In: Computer Vision and Pattern Recognition, pp. 274–280 (1999)Google Scholar
  16. 16.
    Viola, P., Jones, M.: Rapid Object Detection using a Boosted Cascade of Simple Features. In: IEEE Conf. on Computer Vision and Pattern Recognition (2001)Google Scholar
  17. 17.
    Herman, S.-M.: A particle filtering approach to joint passive radar tracking and target classification, PhD thesis, University of Illinois at Urbana-Champaign, 51-54 (2002)Google Scholar
  18. 18.
    Bloom, H.A.P., Bar-Shalom, Y.: The interactive multiple model algorithm for systems with Markovian switching coefficients. IEEE Trans. Automatic Control 33, 780–783 (1988)CrossRefGoogle Scholar
  19. 19.
    Watson, G.A., Blair, W.D.: IMM algorithm for tracking targets that maneuver through coordinated turns. In: Proc. of SPIE Signal and Data Processing of Small Targets, vol. 1698, pp. 236–247 (1992)Google Scholar
  20. 20.
    Kasturi, R. et al.: Performance evaluation protocol for face. In: Person and vehicle detection & tracking in video analysis and content extraction (VACE-II), University of South Florida (Jan. 2006)Google Scholar
  21. 21.
    Isard, M., Blake, A.: CONDENSATION - conditional density propagation for visual tracking. Int. J. Computer Vision 29, 5–28 (1998)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Aristodemos Pnevmatikakis
    • 1
  • Lazaros Polymenakos
    • 1
  • Vasileios Mylonakis
    • 1
  1. 1.Athens Information Technology, Autonomic and Grid Computing, P.O. Box 64, Markopoulou Ave., 19002 PeaniaGreece

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