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Handbook of Biometrics for Forensic Science pp 267–288Cite as

Locating People in Surveillance Video Using Soft Biometric Traits

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

Abstract

Semantic descriptions are a commonly used and very natural way for people to describe one another. Descriptions comprising details of clothing types and colours, skin and hair colour, gender and build are very effective ways to communicating an approximate appearance; however such descriptions are not easily utilised within intelligent video surveillance systems, as they are difficult to transform into a representation that can be utilised by computer vision algorithms. In this chapter, we will investigate two recent approaches to using these semantic, soft biometric descriptions to automatically locate people in surveillance imagery. We present the strengths and weaknesses of each, and discuss their suitability for real-world deployment and how they may be further improved.

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Notes

  1. 1.

    The lookup table is pre-computed for computational efficiency.

  2. 2.

    both approaches are running on a single core of an Intel Xeon E5-2670 processor.

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Author information

Authors and Affiliations

  1. Image and Video Research Laboratory, Queensland University of Technology, 2 George Street, Brisbane, 4000, Australia

    Simon Denman, Michael Halstead, Clinton Fookes & Sridha Sridharan

Authors
  1. Simon Denman
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  2. Michael Halstead
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  3. Clinton Fookes
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  4. Sridha Sridharan
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Corresponding author

Correspondence to Simon Denman .

Editor information

Editors and Affiliations

  1. University of Sassari, Alghero, Sassari, Italy

    Massimo Tistarelli

  2. University of Lausanne, Lausanne, Switzerland

    Christophe Champod

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Denman, S., Halstead, M., Fookes, C., Sridharan, S. (2017). Locating People in Surveillance Video Using Soft Biometric Traits. In: Tistarelli, M., Champod, C. (eds) Handbook of Biometrics for Forensic Science. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-50673-9_12

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  • DOI: https://doi.org/10.1007/978-3-319-50673-9_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50671-5

  • Online ISBN: 978-3-319-50673-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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