Multifactor feature extraction for human movement recognition

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

  • Affiliations:
  • School of Arts, Media and Engineering, Arizona State University Tempe, AZ 85287, USA and School of Electrical, Computer and Energy Engineering, Arizona State University Tempe, AZ 85287, USA;School of Arts, Media and Engineering, Arizona State University Tempe, AZ 85287, USA and School of Electrical, Computer and Energy Engineering, Arizona State University Tempe, AZ 85287, USA;Honeywell Labs, 1985 Douglas Drive North, Golden Valley, MN 55422, USA;School of Computing, Informatics, and Decision Systems Engineering, Arizona State University Tempe, AZ 85287, USA

  • Venue:
  • Computer Vision and Image Understanding
  • Year:
  • 2011

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Abstract

In this paper, we systematically examine multifactor approaches to human pose feature extraction and compare their performances in movement recognition. Two multifactor approaches have been used in pose feature extraction, including a deterministic multilinear approach and a probabilistic approach based on multifactor Gaussian process. These two approaches are compared in terms of the degrees of view-invariance, reconstruction capacity, performances in human pose and gesture recognition using real movement datasets. The experimental results show that the deterministic multilinear approach outperforms the probabilistic-based approach in movement recognition.