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A Particle Filter Framework for Object Tracking Using Visual-Saliency Information

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Intelligent Multimedia Surveillance

Abstract

Automated processing of video streams is core to current surveillance systems. The basic building blocks of video processing techniques are object detection and tracking. Tracking results are further analyzed to detect various events and activities for situation assessment. Several approaches to object detection and tracking are based on background modeling. These approaches are generally vulnerable to noise, illumination changes etc. Further, the object may not look similar in an image sequence over time due to changes in orientation, lighting, occlusion, etc. In this chapter, we explore application of neurobiology-saliency for object detection and tracking using particle filters. We use low-level features such as color, luminance and edge information along with motion cues to track a single person. Experimental results show that this approach is illumination invariant and can track persons in varying lighting conditions.

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Author information

Authors and Affiliations

  1. Department of Computer Science, ETH Zürich, Zürich, Switzerland

    Dwarikanath Mahapatra

  2. School of Computing, National University of Singapore, Singapore, Singapore

    Mukesh Saini

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  1. Dwarikanath Mahapatra
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  2. Mukesh Saini
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Correspondence to Dwarikanath Mahapatra .

Editor information

Editors and Affiliations

  1. Department of Applied Computer Science, University of Winnipeg, Winnipeg, Canada

    Pradeep K. Atrey

  2. Dept. of Computer Science, National University of Singapore, Singapore, Singapore

    Mohan S. Kankanhalli

  3. Elec. Engineering and Computer Science, Queen Mary University of London, London, United Kingdom

    Andrea Cavallaro

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Mahapatra, D., Saini, M. (2013). A Particle Filter Framework for Object Tracking Using Visual-Saliency Information. In: Atrey, P., Kankanhalli, M., Cavallaro, A. (eds) Intelligent Multimedia Surveillance. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41512-8_7

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  • DOI: https://doi.org/10.1007/978-3-642-41512-8_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41511-1

  • Online ISBN: 978-3-642-41512-8

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