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A comprehensive study towards high-level approaches for weapon detection using classical machine learning and deep learning methods
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AI
DOI：10.1016/j.eswa.2022.118698.png)
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摘要
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Surveillance systems do not give a rapid response to deal with suspicious activities such as armed robbery in public places. Consequently, there is a need for technology that can recognize criminal activities from Closed Circuit Televisions (CCTV) footage without the need of human help. Various high-performance computing algorithms have been developed but are limited to specific conditions. In this paper, we have identified gaps between existing technologies for weapon detection. The automatic detection of guns/weapons could help in the investigation of crime scenes. A new and difficult area of study is identifying the specific type of firearm used in an attack known as intra-class detection. The study examines and classifies the strengths and shortcomings of several existing algorithms using classical machine learning and deep learning approaches, employed in the detection of different kinds of weapons. We have thoroughly compare and analyze the performance of several recent state-of-the-art methods on different datasets along with their future scope. We observed that deep learning techniques beat traditional machine learning techniques in terms of speed and accuracy.
Keyword:
Weapon detection
Deep learning
Machine learning
Computer vision
Security and surveillance
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
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