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HLAC between Cells of HOG Feature for Crowd Counting

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Book cover Advances in Visual Computing (ISVC 2014)

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

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Abstract

This paper proposes a crowd counting method using higher order auto-correlation (HLAC) feature between cells of histogram oriented gradient (HOG). Although HOG feature is effective for human detection, it depends on the object position and is not suitable for crowd counting. To apply HOG feature to crowd counting, we extract the first-order HLAC feature from cells of HOG feature. Our new feature has shift invariance and additive properties of HLAC feature as well as the robustness to illumination variation of HOG feature. We predict the number of humans in an image using partial least squares regression (PLSR) from our feature. We evaluate our method using the Mall dataset, and we confirmed that our method gives the state-of-art performance.

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Author information

Authors and Affiliations

  1. Meijo University, Japan

    Shohei Kumagai & Kazuhiro Hotta

Authors
  1. Shohei Kumagai
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  2. Kazuhiro Hotta
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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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Kumagai, S., Hotta, K. (2014). HLAC between Cells of HOG Feature for Crowd Counting. 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_66

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  • DOI: https://doi.org/10.1007/978-3-319-14249-4_66

  • 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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