Advertisement

A Data Fusion Perspective on Human Motion Analysis Including Multiple Camera Applications

  • Rodrigo Cilla
  • Miguel A. Patricio
  • Antonio Berlanga
  • José M. Molina
Conference paper
  • 1.3k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7931)

Abstract

Human motion analysis methods have received increasing attention during the last two decades. In parallel, data fusion technologies have emerged as a powerful tool for the estimation of properties of objects in the real world. This papers presents a view of human motion analysis from the viewpoint of data fusion. JDL process model and Dasarathy’s input-output hierarchy are employed to categorize the works in the area. A survey of the literature in human motion analysis from multiple cameras is included. Future research directions in the area are identified after this review.

Keywords

Human Action Recognition Data Fusion Computer Vision 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aggarwal, J.K., Ryoo, M.S.: Human activity analysis. ACM Computing Surveys 43(3), 1–43 (2011)CrossRefGoogle Scholar
  2. 2.
    Castanedo, F., Gomez-Romero, J., Patricio, M.A., Garcia, J., Molina, J.M.: Distributed data and information fusion in visual sensor networks. In: Distributed Data Fusion for Network-Centric Operations, p. 435 (2012)Google Scholar
  3. 3.
    Chen, D., Chou, P.C., Fookes, C.B.: Multi-view human pose estimation using modified five-point skeleton model, pp. 17–19 (2008)Google Scholar
  4. 4.
    Cilla, R., Patricio, M.A., Berlanga, A., Molina, J.M.: Multicamera action recognition with canonical correlation analysis and discriminative sequence classification. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds.) IWINAC 2011, Part I. LNCS, vol. 6686, pp. 491–500. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  5. 5.
    Cilla, R., Patricio, M.A., Berlanga, A., Molina, J.M.: A probabilistic, discriminative and distributed system for the recognition of human actions from multiple views. Neurocomputing 75(1), 78–87 (2012)CrossRefGoogle Scholar
  6. 6.
    Dasarathy, B.V.: Sensor fusion potential exploitation-innovative architectures and illustrative applications. Proceedings of the IEEE 85(1), 24–38 (1997)CrossRefGoogle Scholar
  7. 7.
    Gkalelis, N., Kim, H., Hilton, A., Nikolaidis, N., Pitas, I.: The i3DPost Multi-View and 3D Human Action/Interaction Database. In: 2009 Conference for Visual Media Production, pp. 159–168 (November 2009)Google Scholar
  8. 8.
    Gómez-Romero, J., Serrano, M.A., Patricio, M.A., García, J., Molina, J.M.: Context-based scene recognition from visual data in smart homes: an information fusion approach. Personal and Ubiquitous Computing, 1–23 (2011)Google Scholar
  9. 9.
    Holte, M.B., Chakraborty, B.: A Local 3D Motion Descriptor for Multi-View Human Action Recognition from 4D Spatio-Temporal Interest Points, vol. (c), pp. 1–13 (2011)Google Scholar
  10. 10.
    Holte, M.B., Moeslund, T.B., Nikolaidis, N., Pitas, I.: 3D Human Action Recognition for Multi-view Camera Systems. In: 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission, pp. 342–349 (May 2011)Google Scholar
  11. 11.
    Iosifidis, A., Tefas, A., Nikolaidis, N., Pitas, I.: Multi-view human movement recognition based on fuzzy distances and linear discriminant analysis. Computer Vision and Image Understanding 116(3), 347–360 (2012)CrossRefGoogle Scholar
  12. 12.
    Karthikeyan, S., Gaur, U., Manjunath, B.S., Grafton, S.: Probabilistic subspace-based learning of shape dynamics modes for multi-view action recognition. In: 2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops, pp. 1282–1286 (November 2011)Google Scholar
  13. 13.
    Kautz, H., Allen, J.F.: Generalized plan recognition. In: Proceedings of the Fifth National Conference on Artificial Intelligence, Philadelphia, PA, vol. 19, p. 86 (1986)Google Scholar
  14. 14.
    Liggins, M.E., Hall, D.L., Llinas, J.: Handbook of multisensor data fusion: theory and practice, vol. 22. CRC (2008)Google Scholar
  15. 15.
    Määttä, T., Aghajan, H.: On efficient use of multi-view data for activity recognition, pp. 158–165 (2010)Google Scholar
  16. 16.
    Naiel, M.A., Abdelwahab, M.M.: Multi-view Human Action Recognition System Employing 2DPCA Motaz El-Saban, pp. 270–275 (2010)Google Scholar
  17. 17.
    Pehlivan, S., Duygulu, P.: A new pose-based representation for recognizing actions from multiple cameras. Computer Vision and Image Understanding 115(2), 140–151 (2011)CrossRefGoogle Scholar
  18. 18.
    Peng, B., Qian, G., Rajko, S.: View-invariant full-body gesture recognition via multilinear analysis of voxel data. In: 2009 Third ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC, pp. 1–8 (August 2009)Google Scholar
  19. 19.
    Ramagiri, S., Kavi, R., Kulathumani, V.: Real-time multi-view human action recognition using a wireless camera network. In: 2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras, pp. 1–6 (August 2011)Google Scholar
  20. 20.
    Rudoy, D., Zelnik-Manor, L.: Viewpoint Selection for Human Actions. International Journal of Computer Vision 97(3), 243–254 (2011)CrossRefGoogle Scholar
  21. 21.
    Shen, C., Zhang, C., Fels, S.: A Multi-Camera Surveillance System that Estimates Quality-of-View Measurement. In: 2007 IEEE International Conference on Image Processing, pp. III-193–III-196 (2007)Google Scholar
  22. 22.
    Srivastava, G., Iwaki, H., Park, J., Kak, A.C.: Distributed and lightweight multi-camera human activity classification. In: 2009 Third ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC, pp. 1–8 (August 2009)Google Scholar
  23. 23.
    Steinberg, A.N., Bowman, C.L., White, F.E.: Revisions to the JDL data fusion model. American Inst. of Aeronautics and Astronautics, New York (1998)Google Scholar
  24. 24.
    Wang, Y., Huang, K., Tan, T.: Multi-view Gymnastic Activity Recognition with Fused HMM, pp. 667–677 (2007)Google Scholar
  25. 25.
    White, F., et al.: A model for data fusion. In: Proc. 1st National Symposium on Sensor Fusion, vol. 2, pp. 149–158 (1988)Google Scholar
  26. 26.
    Wu, C., Khalili, A.H., Aghajan, H.: Multiview activity recognition in smart homes with spatio-temporal features. In: Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2010, p. 142 (2010)Google Scholar
  27. 27.
    Yan, P., Khan, S.M., Shah, M.: Learning 4D action feature models for arbitrary view action recognition. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (June 2008)Google Scholar
  28. 28.
    Zhu, F., Shao, L., Lin, M.: Multi-View Action Recognition Using Local Similarity Random Forests and Sensor Fusion. Pattern Recognition Letters (May 2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Rodrigo Cilla
    • 1
  • Miguel A. Patricio
    • 1
  • Antonio Berlanga
    • 1
  • José M. Molina
    • 1
  1. 1.Computer Science DepartmentUniversidad Carlos III de MadridColmenarejoSpain

Personalised recommendations