Automated Detection of Firearms and Knives in a CCTV Image
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/16/1/47 |
| _version_ | 1818015424474775552 |
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| author | Michał Grega Andrzej Matiolański Piotr Guzik Mikołaj Leszczuk |
| author_facet | Michał Grega Andrzej Matiolański Piotr Guzik Mikołaj Leszczuk |
| author_sort | Michał Grega |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T06:57:51Z |
| format | Article |
| id | doaj.art-3f539dfb764a49a6b0258af184187e69 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T06:57:51Z |
| publishDate | 2016-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3f539dfb764a49a6b0258af184187e692022-12-22T02:06:50ZengMDPI AGSensors1424-82202016-01-011614710.3390/s16010047s16010047Automated Detection of Firearms and Knives in a CCTV ImageMichał Grega0Andrzej Matiolański1Piotr Guzik2Mikołaj Leszczuk3AGH University of Science and Technology, al. Mickiewicza 30, Krakow 30-059, PolandAGH University of Science and Technology, al. Mickiewicza 30, Krakow 30-059, PolandAGH University of Science and Technology, al. Mickiewicza 30, Krakow 30-059, PolandAGH University of Science and Technology, al. Mickiewicza 30, Krakow 30-059, PolandClosed 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.http://www.mdpi.com/1424-8220/16/1/47Haar cascadeOpenCVpattern recognitionfuzzy classifierdata analysisfeature descriptorknife detectionfirearm detection |
| spellingShingle | Michał Grega Andrzej Matiolański Piotr Guzik Mikołaj Leszczuk Automated Detection of Firearms and Knives in a CCTV Image Sensors Haar cascade OpenCV pattern recognition fuzzy classifier data analysis feature descriptor knife detection firearm detection |
| title | Automated Detection of Firearms and Knives in a CCTV Image |
| title_full | Automated Detection of Firearms and Knives in a CCTV Image |
| title_fullStr | Automated Detection of Firearms and Knives in a CCTV Image |
| title_full_unstemmed | Automated Detection of Firearms and Knives in a CCTV Image |
| title_short | Automated Detection of Firearms and Knives in a CCTV Image |
| title_sort | automated detection of firearms and knives in a cctv image |
| topic | Haar cascade OpenCV pattern recognition fuzzy classifier data analysis feature descriptor knife detection firearm detection |
| url | http://www.mdpi.com/1424-8220/16/1/47 |
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