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Asian Conference on Computer Vision

ACCV 2014: Computer Vision -- ACCV 2014 pp 50–65Cite as

Discriminative Orderlet Mining for Real-Time Recognition of Human-Object Interaction

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9007))

Abstract

This paper presents a novel visual representation, called orderlets, for real-time human action recognition with depth sensors. An orderlet is a middle level feature that captures the ordinal pattern among a group of low level features. For skeletons, an orderlet captures specific spatial relationship among a group of joints. For a depth map, an orderlet characterizes a comparative relationship of the shape information among a group of subregions. The orderlet representation has two nice properties. First, it is insensitive to small noise since an orderlet only depends on the comparative relationship among individual features. Second, it is a frame-level representation thus suitable for real-time online action recognition. Experimental results demonstrate its superior performance on online action recognition and cross-environment action recognition.

G. Yu—The work was done when Gang Yu was an intern at Microsoft Research. This work is supported in part by Singapore MoE Tier-1 grant.

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Notes

  1. 1.

    The dataset can be downloaded from http://research.microsoft.com/en-us/um/people/zliu/ActionRecoRsrc/default.htm.

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Author information

Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Gang Yu & Junsong Yuan

  2. Microsoft Research, Redmond, WA, USA

    Zicheng Liu

Authors
  1. Gang Yu
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  2. Zicheng Liu
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  3. Junsong Yuan
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Corresponding author

Correspondence to Gang Yu .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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© 2015 Springer International Publishing Switzerland

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Yu, G., Liu, Z., Yuan, J. (2015). Discriminative Orderlet Mining for Real-Time Recognition of Human-Object Interaction. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9007. Springer, Cham. https://doi.org/10.1007/978-3-319-16814-2_4

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  • DOI: https://doi.org/10.1007/978-3-319-16814-2_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16813-5

  • Online ISBN: 978-3-319-16814-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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