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International Symposium on Visual Computing

ISVC 2015: Advances in Visual Computing pp 613–623Cite as

Gradient Local Auto-Correlations and Extreme Learning Machine for Depth-Based Activity Recognition

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9474))

Abstract

This paper presents a new method for human activity recognition using depth sequences. Each depth sequence is represented by three depth motion maps (DMMs) from three projection views (front, side and top) to capture motion cues. A feature extraction method utilizing spatial and orientational auto-correlations of image local gradients is introduced to extract features from DMMs. The gradient local auto-correlations (GLAC) method employs second order statistics (i.e., auto-correlations) to capture richer information from images than the histogram-based methods (e.g., histogram of oriented gradients) which use first order statistics (i.e., histograms). Based on the extreme learning machine, a fusion framework that incorporates feature-level fusion into decision-level fusion is proposed to effectively combine the GLAC features from DMMs. Experiments on the MSRAction3D and MSRGesture3D datasets demonstrate the effectiveness of the proposed activity recognition algorithm.

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Acknowledgement

We acknowledge the support of the Industry, Teaching and Research Prospective Project of Jiangsu Province (grant No. BY2015027-12), the Natural Science Foundation of China, under contracts 61063021, 61272052 and 61473086, and the Program for New Century Excellent Talents of the University of Ministry of Education of China.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Dallas, Richardson, TX, USA

    Chen Chen

  2. School of Information Science and Engineering, Changzhou University, Changzhou, China

    Zhenjie Hou

  3. School of Automation Science and Electrical Engineering, Beihang University, Beijing, China

    Baochang Zhang & Yun Yang

  4. School of Computer Science, China University of Geosciences, Wuhan, China

    Junjun Jiang

Authors
  1. Chen Chen
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  2. Zhenjie Hou
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  3. Baochang Zhang
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  4. Junjun Jiang
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  5. Yun Yang
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Corresponding author

Correspondence to Zhenjie Hou .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

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

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. University of Houston, Houston, Texas, USA

    Ioannis Pavlidis

  6. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA

    Rogerio Feris

  7. Purdue University, West Lafayette, Indiana, USA

    Tim McGraw

  8. Side Effects Software, Santa Monica, California, USA

    Mark Elendt

  9. The DiVE, Durham, North Carolina, USA

    Regis Kopper

  10. Texas A&M University, College Station, Texas, USA

    Eric Ragan

  11. Kent State University, Kent, Ohio, USA

    Zhao Ye

  12. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Gunther Weber

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© 2015 Springer International Publishing Switzerland

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Chen, C., Hou, Z., Zhang, B., Jiang, J., Yang, Y. (2015). Gradient Local Auto-Correlations and Extreme Learning Machine for Depth-Based Activity Recognition. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9474. Springer, Cham. https://doi.org/10.1007/978-3-319-27857-5_55

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  • DOI: https://doi.org/10.1007/978-3-319-27857-5_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27856-8

  • Online ISBN: 978-3-319-27857-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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