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Binocular Full-Body Pose Recognition and Orientation Inference Using Multilinear Analysis

  • Chapter
Tensors in Image Processing and Computer Vision

Part of the book series: Advances in Pattern Recognition ((ACVPR))

Abstract

In this chapter, we propose an approach to full-body pose recognition and body orientation estimation using multilinear analysis. We extract low-dimensional pose and body orientation coefficient vectors by performing tensor decomposition and projection on silhouette images obtained from wide baseline binocular cameras. The coefficient vectors are then used as feature vectors in pose recognition and body orientation estimation. To do pose recognition, pose coefficient vectors obtained from synthesized pose silhouettes are used to train a family of support vector machines as pose classifiers. Using orientation coefficient vectors, a 1-D orientation manifold is learned and further used for the estimation of body orientation. Experiment results obtained using both synthetic and real image data showed that the performance of our approach is comparable to existing pose recognition approaches, and that our approach outperformed the traditional tensor-based recognition approach in the comparative test.

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Authors and Affiliations

  1. Arts, Media & Engineering Program, and Department of Electrical Engineering, Arizona State University, Tempe, AZ 85287

    Bo Peng & Gang Qian

Authors
  1. Bo Peng
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  2. Gang Qian
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Correspondence to Bo Peng .

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Editors and Affiliations

  1. Escuela Técnica Superior de Ingenieros de Telecomunicacíon, Universidad de Valladolid, Campus Miguel Delibes, 47011 Valladolid, Spain

    Santiago Aja-Fernández  & Rodrigo de Luis García  & 

  2. Dept. Computing, HongKong Polytechnic University, Hong Kong

    Dacheng Tao

  3. University of London Birkbeck College School of Computer Science & Information Systems, Malet Street, London, WC1E 7HX, UK

    Xuelong Li

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© 2009 Springer-Verlag London Limited

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Peng, B., Qian, G. (2009). Binocular Full-Body Pose Recognition and Orientation Inference Using Multilinear Analysis. In: Aja-Fernández, S., de Luis García, R., Tao, D., Li, X. (eds) Tensors in Image Processing and Computer Vision. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-84882-299-3_10

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  • DOI: https://doi.org/10.1007/978-1-84882-299-3_10

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84882-298-6

  • Online ISBN: 978-1-84882-299-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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