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Human Action Recognition Using Histograms of Oriented Optical Flows from Depth

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Advances in Visual Computing (ISVC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8887))

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Abstract

In this paper we develop a new method for recognizing human actions from depth data. 2D optical flows from depth images are computed for the entire action instance. From the resulting optical flow vectors, patches are defined around each joint location to learn local motion variations. These patches are grouped in terms of their joints and used to extract a new feature called ‘Histograms of Oriented Optical Flows from Depth (HOOFD)’. In order to encode temporal variations, these features are generated in a pyramidal fashion. At each level of the pyramid, action instance is partitioned equally into two parts and each part is employed separately to compute histograms. Oriented optical flow histograms are utilized due to their invariance to scale and direction of motion. We performed several experiments on publicly available databases and compared our approach with some of the state-of-the-art methods. Results show the success of the proposed method.

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Author information

Authors and Affiliations

  1. Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey

    Baris Can Ustundag & Mustafa Unel

Authors
  1. Baris Can Ustundag
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  2. Mustafa Unel
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Editor information

Editors and Affiliations

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

    George Bebis

  2. NASA Ames Research Center, Moffett Field, 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 University of Texas at Dallas, 75080, Richardson, TX, USA

    Ryan McMahan

  6. NextGen Interactions, 27604, Raleigh, NC, USA

    Jason Jerald

  7. Indiana University, 46202, Indianapolis, IN, USA

    Hui Zhang

  8. Microsoft Research, 1 Microsoft Way, 98052, Redmond, WA, USA

    Steven M. Drucker

  9. University of Delaware, 19716-2712, Newark, DE, USA

    Chandra Kambhamettu

  10. Intel Corp., 95054, Santa Clara, CA, USA

    Maha El Choubassi

  11. Computer Graphics and Interactive Media Lab, Department of Computer Science, University of Houston, 77004, Houston, TX, USA

    Zhigang Deng

  12. NVIDIA, 34788, Leesburg, FL, USA

    Mark Carlson

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© 2014 Springer International Publishing Switzerland

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Ustundag, B.C., Unel, M. (2014). Human Action Recognition Using Histograms of Oriented Optical Flows from Depth. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8887. Springer, Cham. https://doi.org/10.1007/978-3-319-14249-4_60

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  • DOI: https://doi.org/10.1007/978-3-319-14249-4_60

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14248-7

  • Online ISBN: 978-3-319-14249-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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