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Person and Vehicle Tracking in Surveillance Video

  • Andrew Miller
  • Arslan Basharat
  • Brandyn White
  • Jingen Liu
  • Mubarak Shah
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4625)

Abstract

This evaluation for person and vehicle tracking in surveillance presented some new challenges. The dataset was large and very high-quality, but with difficult scene properties involving illumination changes, unusual lighting conditions, and complicated occlusion of objects.

Since this is a well-researched scenario [1], our submission was based primarily on our existing projects for automated object detection and tracking in surveillance. We also added several new features that are practical improvements for handling the difficulties of this dataset.

Keywords

Support Vector Machine Particle Swarm Optimization Surveillance Video Illumination Change Edge Pixel 
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.
    Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Comput. Surv. (2006)Google Scholar
  2. 2.
    Javed, O., Shah, M.: Tracking and object classification for automated surveillance. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 343–357. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  3. 3.
    Javed, O., Shafique, K., Shah, M.: A hierarchical approach to robust background subtraction using color and gradient information. In: IEEE Workshop on Motion and Video Computing, Orlando (2002)Google Scholar
  4. 4.
    Sheikh, Y., Shah, M.: Bayesian modeling of dynamic scenes for object detection. PAMI (2005)Google Scholar
  5. 5.
    Shafique, K., Shah, M.: A noniterative greedy algorithm for multiframe point correspondence. IEEE Trans. Pattern Anal. Mach. Intell. (2005)Google Scholar
  6. 6.
    White, B., Shah, M.: Automatically tuning background subtraction parameters using particle swarm optimization. In: IEEE International Conference on Multimedia and Expo., Beijing, China (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Andrew Miller
    • 1
  • Arslan Basharat
    • 1
  • Brandyn White
    • 1
  • Jingen Liu
    • 1
  • Mubarak Shah
    • 1
  1. 1.Computer Vision Lab at University of Central Florida 

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