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A Block-Based Human Model for Visual Surveillance

  • Encarnación Folgado
  • Mariano Rincón
  • Margarita Bachiller
  • Enrique J. Carmona
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5602)

Abstract

This paper presents BB6-HM, a block-based human model for real-time monitoring of a large number of visual events and states related to human activity analysis, which can be used as components of a library to describe more complex activities in such important areas as surveillance. BB6-HM is inspired by the proportionality rules commonly used in Visual Arts, i.e., for dividing the human silhouette into six rectangles of the same height. The major advantage of this proposal is that analysis of the human can be easily broken down into parts, which allows us to introduce more specific domain knowledge and to reduce the computational load. It embraces both frontal and lateral views, is a fast and scale-invariant method and a large amount of task-focused information can be extracted from it.

Keywords

Lateral View Human Model Block Model Reference Axis Human Activity Recognition 
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.
    Ben-Arie, J., Wang, Z., Pandit, P., Rajaram, S.: Human Activity Recognition Using Multidimensional Indexing. IEEE Trans. on Pattern Analysis and machine Intelligence 24(8), 1091–1104 (2002)CrossRefGoogle Scholar
  2. 2.
    Carmona, E.J., Martínez-Cantos, J., Mira, J.: A new video segmentation method of moving objects based on blob-level knowledge. Pattern Recognition Letters 29(3), 272–285 (2008)CrossRefGoogle Scholar
  3. 3.
    Carmona, E.J., Rincón, M., Bachiller, M., Martínez-Cantos, J., Martinez-Tomas, R., Mira, J.: On the effect of feedback in multilevel representation spaces for visual surveillance tasks. Neurocomputing 72(4-6), 916–927 (2009)CrossRefGoogle Scholar
  4. 4.
    Gavrila, D.: The Visual Analysis of Human Movement: A Survey. Computer Vision and Image Understanding 73(1), 82–98 (1999)CrossRefzbMATHGoogle Scholar
  5. 5.
    Haritaoglu, I., Harwood, D., Davis, L.S.: W4: Real-Time Surveillance of People and Their Activities. IEEE Trans. on Pattern Analysis and machine Intelligence 22(8), 809–830 (2000)CrossRefGoogle Scholar
  6. 6.
    Martinez-Tomas, R., Rincón, M., Bachiller, M., Mira, J.: On the Correspondence between Objects and Events for the Diagnosis of Situations in Visual Surveillance Tasks. Pattern Recognition Letters 29(8), 1117–1135 (2008)CrossRefGoogle Scholar
  7. 7.
    Wren, C.R., Azarbayejani, A., Darrell, T., Pentland, A.P.: Pfinder: Real-Time Tracking of the Human Body. IEEE Trans. on Pattern Analysis and machine Intelligence 19(7), 780–785 (1997)CrossRefGoogle Scholar
  8. 8.
    Zhang, J., Collins, R., Liu, Y.: Representation and Matching of Articulated Shapes. In: International Conference on Computer Vision and Pattern Recognition, pp. 342–349 (2004)Google Scholar
  9. 9.
    Zhao, T., Nevatia, R.: Tracking Multiple Humans in Complex Situations. IEEE Trans. on Pattern Analysis and machine Intelligence 26(9), 1208–1221 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Encarnación Folgado
    • 1
  • Mariano Rincón
    • 1
  • Margarita Bachiller
    • 1
  • Enrique J. Carmona
    • 1
  1. 1.Departamento de Inteligencia Artificial. Escuela Técnica Superior de Ingeniería InformáticaUniversidad Nacional de Educación a DistanciaMadridSpain

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