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Multisensor Concealed Weapon Detection Using the Image Fusion Approach

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Presentation
  • Session
  • Friday, 17 July 2015
  • 12:17 - 12:17
  • Duration: 17 mins
  • Publication date: 24 Jul 2015
  • Location: NA, Queen Mary University, London, United Kingdom
  • Part of event Imaging for Crime Prevention and Detection 2015

About the session

Jonathan Wu

Organised by the Vision and Imaging TPN.

Crime and anti-social behaviour have a significant cost for society and business alike. Just in the UK anti-social behaviour alone accounts annually for around £3.4 billion of taxpayers’ money with incidents of graffiti and vandalism estimated to cost around £600 million/p.a. Surveillance systems of all kinds are thus extensively deployed in public and private locations to deter, prevent and control. The last years have also seen an increased awareness on the vulnerability of public spaces to attacks. However, there are serious limitations to the use of conventional monitoring systems where human operators are asked to survey a large number of cameras or go through enormous amounts of recorded material for forensic investigations. Computer-based technologies are increasingly becoming researched in what is now known as video analytics, propelled by advances in processing power, embedded computing, IP-networking technologies, volume storage, cheap cameras, etc. The realisation of such advances into working systems can have a major impact on society but also on individual liberty. 

This conference follows the successful IDSS (Intelligent Distributed Surveillance Systems) events held in 2003 and 2004 and ICDP 2005, 2006, 2009, 2011 and 2013 to bring together researchers, industry, end-users, lawenforcing agencies and citizens groups to share experiences and explore areas where additional research and development are needed, identify possible collaboration and consider the societal impact of such technologies.

Keywords:
  • ICDP
  • concealed weapon
  • detection
  • frequency
  • high-frequency
  • weapon
  • weapons

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  • QW

    Q. M Jonathan Wu

    University of Windsor

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