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Modelling Spatial Correlation and Image Statistics for Improved Tracking of Human Gestures

  • Conference paper
Pattern Recognition and Image Analysis (IbPRIA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3522))

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Abstract

In this paper, we examine sensor specific distributions of local image operators (edge and line detectors), which describe the appearance of people in video sequences. The distributions are used to describe a probabilistic articulated motion model to track the gestures of a person in terms of arms and body movement, which is solved using a particle filter. We focus on modeling the statistics of one sensor and examine the influence of image noise and scale, and the spatial accuracy that is obtainable. Additionally spatial correlation between pixels is modeled in the appearance model. We show that by neglecting the correlation high detection probabilities are quickly overestimated, which can often lead to false positives. Using the weighted geometric mean of pixel information leads to much improved results.

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Author information

Authors and Affiliations

  1. Department of Telecommunications and Information Processing, University of Ghent, St.-Pietersnieuwstraat 41, B-9000, Gent, Belgium

    Rik Bellens, Sidharta Gautama & Johan D’Haeyer

Authors
  1. Rik Bellens
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  2. Sidharta Gautama
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  3. Johan D’Haeyer
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Editor information

Editors and Affiliations

  1. Instituto Superior Técnico & Instituto de Sistemas e Robótica, 1049-001, Lisboa, Portugal

    Jorge S. Marques

  2. ETSI Informática y e Telecomunicación, University of Granada, 18071, Granada, Spain

    Nicolás Pérez de la Blanca

  3. CERENA-Centro de Recursos Naturais e Ambiente, Instituto Superior Técnico, Av. Rovisco Pais, 1049-001, Lisboa, Portugal

    Pedro Pina

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© 2005 Springer-Verlag Berlin Heidelberg

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Bellens, R., Gautama, S., D’Haeyer, J. (2005). Modelling Spatial Correlation and Image Statistics for Improved Tracking of Human Gestures. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3522. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492429_66

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  • DOI: https://doi.org/10.1007/11492429_66

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26153-7

  • Online ISBN: 978-3-540-32237-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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