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Sensors 2016, 16(1), 47; doi:10.3390/s16010047

Automated Detection of Firearms and Knives in a CCTV Image

AGH University of Science and Technology, al. Mickiewicza 30, Krakow 30-059, Poland
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Author to whom correspondence should be addressed.
Academic Editor: Murali Subbarao
Received: 30 July 2015 / Revised: 26 October 2015 / Accepted: 18 November 2015 / Published: 1 January 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [1565 KB, uploaded 2 January 2016]   |  

Abstract

Closed circuit television systems (CCTV) are becoming more and more popular and are being deployed in many offices, housing estates and in most public spaces. Monitoring systems have been implemented in many European and American cities. This makes for an enormous load for the CCTV operators, as the number of camera views a single operator can monitor is limited by human factors. In this paper, we focus on the task of automated detection and recognition of dangerous situations for CCTV systems. We propose algorithms that are able to alert the human operator when a firearm or knife is visible in the image. We have focused on limiting the number of false alarms in order to allow for a real-life application of the system. The specificity and sensitivity of the knife detection are significantly better than others published recently. We have also managed to propose a version of a firearm detection algorithm that offers a near-zero rate of false alarms. We have shown that it is possible to create a system that is capable of an early warning in a dangerous situation, which may lead to faster and more effective response times and a reduction in the number of potential victims. View Full-Text
Keywords: Haar cascade; OpenCV; pattern recognition; fuzzy classifier; data analysis; feature descriptor; knife detection; firearm detection Haar cascade; OpenCV; pattern recognition; fuzzy classifier; data analysis; feature descriptor; knife detection; firearm detection
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Grega, M.; Matiolański, A.; Guzik, P.; Leszczuk, M. Automated Detection of Firearms and Knives in a CCTV Image. Sensors 2016, 16, 47.

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