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Asian Conference on Computer Vision

ACCV 2010: Computer Vision – ACCV 2010 pp 149–160Cite as

Change Detection for Temporal Texture in the Fourier Domain

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6492))

Abstract

Research on temporal textures has concerned mainly modeling, synthesis and detection, but not finding changes between different temporal textures. Shot change detection, based on appearance, has received much research attention, but detection of changes between temporal textures has not been addressed sufficiently. Successive temporal textures in a video often have a similar appearance but different motion, a change that shot change detection cannot discern. In this paper, changes between temporal textures are captured by deriving a non-parametric statistical model for the motions via a novel approach, based on properties of the Fourier transform. Motion statistics are used in a sequential change detection test to find changes in the motion distributions, and consequently the temporal textures. Experiments use a wide range of videos of temporal textures, groups of people, traffic. The proposed approach leads to correct change detection, at a low computational cost.

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Author information

Authors and Affiliations

  1. Centre for Research and Technology, Informatics and Telematics Institute, Hellas 6th km, Charilaou-Thermis, Thermi, 57001, Greece

    Alexia Briassouli & Ioannis Kompatsiaris

Authors
  1. Alexia Briassouli
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  2. Ioannis Kompatsiaris
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Technion, Israel Institute of Technology, 32000, Haifa, Israel

    Ron Kimmel

  2. The University of Auckland, 37 Kohimarama Road, 1071, Mission Bay, Auckland, New Zealand

    Reinhard Klette

  3. National Institute of Informatics, 1018430, Chiyoda, Tokyo, Japan

    Akihiro Sugimoto

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© 2011 Springer-Verlag Berlin Heidelberg

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Briassouli, A., Kompatsiaris, I. (2011). Change Detection for Temporal Texture in the Fourier Domain. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19315-6_12

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  • DOI: https://doi.org/10.1007/978-3-642-19315-6_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19314-9

  • Online ISBN: 978-3-642-19315-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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