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International Symposium on Visual Computing

ISVC 2010: Advances in Visual Computing pp 606–615Cite as

View Invariant Activity Recognition with Manifold Learning

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6454))

Abstract

Activity recognition in complex scenes can be very challenging because human actions are unconstrained and may be observed from multiple views. While progress has been made in recognizing activities from fixed views, more research is needed in developing view invariant recognition methods. Furthermore, the recognition and classification of activities involves processing data in the space and time domains, which involves large amounts of data and can be computationally expensive to process. To accommodate for view invariance and high dimensional data we propose the use of Manifold Learning using Locality Preserving Projections (LPP). We develop an efficient set of features based on radial distance and present a Manifold Learning framework for learning low dimensional representations of action primitives that can be used to recognize activities at multiple views. Using our approach we present high recognition rates on the Inria IXMAS dataset.

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Author information

Authors and Affiliations

  1. Computing and Information Sciences and Computer Engineering, Rochester Institute of Technology, Rochester, NY, 14623, USA

    Sherif Azary & Andreas Savakis

Authors
  1. Sherif Azary
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  2. Andreas Savakis
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, 89557, Reno, NV, USA

    George Bebis

  2. Moffett Field, NASA Ames Research Center, 94035, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. The Chinese University of Hong Kong, Shatin, Hong Kong, China

    Ronald Chung

  6. Dyna Vox Systems, Pittsburgh, PA, USA

    Riad Hammound

  7. King Saud University, Riyadh, Saudi Arabia

    Muhammad Hussain

  8. Hewlett Packard Labs, Paolo Alto, CA, USA

    Tan Kar-Han

  9. The Ohio State University, Columbus, OH, USA

    Roger Crawfis

  10. Virtual Reality Lab, EPFL, Lausanne, Switzerland

    Daniel Thalmann

  11. NASA Ames Research Center, Clifton Park, NY, USA

    David Kao

  12. Kitware, Clifton Park, NY, USA

    Lisa Avila

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© 2010 Springer-Verlag Berlin Heidelberg

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Cite this paper

Azary, S., Savakis, A. (2010). View Invariant Activity Recognition with Manifold Learning. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6454. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17274-8_59

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  • DOI: https://doi.org/10.1007/978-3-642-17274-8_59

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17273-1

  • Online ISBN: 978-3-642-17274-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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