Advertisement

Hybrid Hierarchical Learning from Dynamic Scenes

  • Prithwijit Guha
  • Pradeep Vaghela
  • Pabitra Mitra
  • K. S. Venkatesh
  • Amitabha Mukerjee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3776)

Abstract

The work proposes a hierarchical architecture for learning from dynamic scenes at various levels of knowledge abstraction. The raw visual information is processed at different stages to generate hybrid symbolic/sub-symbolic descriptions of the scene, agents and events. The background is incrementally learned at the lowest layer, which is used further in the mid-level for multi-agent tracking with symbolic reasoning. The agent/event discovery is performed at the next higher layer by processing the agent features, status history and trajectory. Unlike existing vision systems, the proposed algorithm does not assume any prior information and aims at learning the scene/agent/event models from the acquired images. This makes it a versatile vision system capable of performing in a wide variety of environments.

Keywords

Gaussian Mixture Model Zernike Moment Dynamic Scene Event Discovery Event Primitive 
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.

References

  1. 1.
    Granlund, G.: Organization of architectures for cognitive vision systems. In: Proceedings of Workshop on Cognitive Vision, Schloss Dagstuhl, Germany (2003)Google Scholar
  2. 2.
    Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 246–252. IEEE Computer Society, Los Alamitos (1999)Google Scholar
  3. 3.
    Gutchess, D., Trajkovics, M., Cohen-Solal, E., Lyons, D., Jain, A.K.: A background model initialization algorithm for video surveillance. In: Proceedings of Eighth IEEE International Conference on Computer Vision, vol. 1, pp. 733–740 (2001)Google Scholar
  4. 4.
    Haritaoglu, I., Harwood, D., Davis, L.: W4: Real time surveillance of people and their activities. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 809–830 (2000)CrossRefGoogle Scholar
  5. 5.
    Guha, P., Mukerjee, A., Venkatesh, K.: Efficient occlusion handling for multiple agent tracking with surveillance event primitives. In: The Second Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (2005)Google Scholar
  6. 6.
    Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Transaction on Pattern Analysis Machine Intelligence 25, 564–575 (2003)CrossRefGoogle Scholar
  7. 7.
    Teague, M.R.: Image analysis via the general theory of moments. Optical Society of America 70, 920–930 (1980)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Galata, A., Johnson, N., Hogg, D.: Learning variable-length markov models of behavior. Computer Vision and Image Understanding 81, 398–413 (2001)zbMATHCrossRefGoogle Scholar
  9. 9.
    Johnson, N., Galata, A., Hogg, D.: The acquisition and use of interaction behavior models. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 866–871. IEEE Computer Society, Los Alamitos (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Prithwijit Guha
    • 1
  • Pradeep Vaghela
    • 1
  • Pabitra Mitra
    • 2
  • K. S. Venkatesh
    • 1
  • Amitabha Mukerjee
    • 2
  1. 1.Department of Electrical EngineeringIndian Institute of Technology, KanpurKanpurIndia
  2. 2.Department of Computer Science and EngineeringIndian Institute of Technology, KanpurKanpurIndia

Personalised recommendations