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Feature Correspondence in Low Quality CCTV Videos

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Emerging Trends and Advanced Technologies for Computational Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 647))

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Abstract

Closed-circuit television cameras are used extensively to monitor streets for the security of the public. Whether passively recording day-to-day life, or actively monitoring a developing situation such as public disorder, the videos recorded have proven invaluable to police forces world wide to trace suspects and victims alike. The volume of video produced from the array of camera covering even a small area is large, and growing in modern society, and post-event analysis of collected video is a time consuming problem for police forces that is increasing. Automated computer vision analysis is desirable, but current systems are unable to reliably process videos from CCTV cameras. The video quality is low, and computer vision algorithms are unable to perform sufficiently to achieve usable results. In this chapter, we describe some of the reasons for the failure of contemporary algorithms and focus on the fundamental task of feature correspondence between frames of video—a well-studied and often considered solved problem in high quality videos, but still a challenge in low quality imagery. We present solutions to some of the problems that we acknowledge, and provide a comprehensive analysis where we demonstrate feature matching using a 138-dimensional descriptor that improves the matching performance of a state-of-the-art 384-dimension colour descriptor with just \(36\,\%\) of the storage requirements.

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Acknowledgments

This work is funded by the European Union’s Seventh Framework Programme, specific topic framework and tools for (semi-) automated exploitation of massive amounts of digital data for forensic purposes, under grant agreement number 607480 (LASIE IP project). The authors also extend their thanks to the Metropolitan Police at Scotland Yard, London, UK, for the supply of and permission to use CCTV images.

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Authors and Affiliations

  1. Multimedia and Vision Lab, School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK

    Craig Henderson & Ebroul Izquierdo

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  1. Craig Henderson
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  2. Ebroul Izquierdo
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Correspondence to Craig Henderson .

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Editors and Affiliations

  1. School of Computer Science, De Montfort University, Leicester, United Kingdom

    Liming Chen

  2. Organization, The Science and Information (SAI), Bradford, West Yorkshire, United Kingdom

    Supriya Kapoor

  3. Organization, The Science and Information (SAI), Bradford, West Yorkshire, United Kingdom

    Rahul Bhatia

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Henderson, C., Izquierdo, E. (2016). Feature Correspondence in Low Quality CCTV Videos. In: Chen, L., Kapoor, S., Bhatia, R. (eds) Emerging Trends and Advanced Technologies for Computational Intelligence. Studies in Computational Intelligence, vol 647. Springer, Cham. https://doi.org/10.1007/978-3-319-33353-3_14

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  • DOI: https://doi.org/10.1007/978-3-319-33353-3_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33351-9

  • Online ISBN: 978-3-319-33353-3

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