Millimetre-wave personnel scanners for automated weapon detection

  • Authors:

  • Beatriz Grafulla-González;Christopher D. Haworth;Andrew R. Harvey;Katia Lebart;Yvan R. Petillot;Yves de Saint-Pern;Mathilde Tomsin;Emanuele Trucco

  • Affiliations:

  • Electrical, Electronic and Computer Engineering Department, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, United Kingdom;Electrical, Electronic and Computer Engineering Department, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, United Kingdom;Electrical, Electronic and Computer Engineering Department, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, United Kingdom;Electrical, Electronic and Computer Engineering Department, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, United Kingdom;Electrical, Electronic and Computer Engineering Department, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, United Kingdom;Electrical, Electronic and Computer Engineering Department, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, United Kingdom;Electrical, Electronic and Computer Engineering Department, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, United Kingdom;Electrical, Electronic and Computer Engineering Department, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, United Kingdom

  • Venue:
  • ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
  • Year:
  • 2005

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Abstract

The ATRIUM project aims to the automatic detection of threats hidden under clothes using millimetre-wave imaging. We describe a simulator of realistic millimetre-wave images and a system for detecting metallic weapons automatically. The latter employs two stages, detection and tracking. We present a detector for metallic objects based on mixture models, and a target tracker based on particle filtering. We show convincing, simulated millimetre-wave images of the human body with and without hidden threats, including a comparison with real images, and very good detection and tracking performance with eight real sequences. (International Workshop on Pattern Recognition for Crime Prevention, Security and Surveillance)