View-Invariant Pose Recognition Using Multilinear Analysis and the Universum

  • Authors:
  • Bo Peng;Gang Qian;Yunqian Ma

  • Affiliations:
  • Arts, Media and Engineering Program and Department of Electrical Engineering, Arizona State University, Tempe, USA AZ 85287-8709;Arts, Media and Engineering Program and Department of Electrical Engineering, Arizona State University, Tempe, USA AZ 85287-8709;Honeywell Labs, Golden Valley, USA MN 55422

  • Venue:
  • ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
  • Year:
  • 2008

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Abstract

This paper presents an approach to full-body human pose recognition. Inputs to the proposed approach are pairs of silhouette images obtained from wide baseline binocular cameras. Through multilinear analysis, low dimensional view-invariant pose coefficient vectors can be extracted from these stereo silhouette pairs. Taking these pose coefficient vectors as features, the Universum method is trained and used for pose recognition. Experiment results obtained using real image data showed the efficacy of the proposed approach.