Detecting the Possession of Harmful Weapons by Humans Through Surveillance System

International Journal of Electronics and Communication Engineering

© 2024 by SSRG - IJECE Journal

Volume 11 Issue 7

Year of Publication : 2024

Authors : Nalini Manogaran, Suresh Annamalai, Malarvizhi Nandagopal, Koteeswaran Seerangan, Balamurugan Balusamy, Francesco Benedetto

10.14445/23488549/IJECE-V11I7P105

How to Cite?

Nalini Manogaran, Suresh Annamalai, Malarvizhi Nandagopal, Koteeswaran Seerangan, Balamurugan Balusamy, Francesco Benedetto, "Detecting the Possession of Harmful Weapons by Humans Through Surveillance System," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 7, pp. 47-52, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I7P105

Abstract:

Security Surveillance is a very tedious and time-consuming job. The system is to automate the task of analyzing video surveillance and alert systems. We will analyze the video feed in real-time and identify abnormal activities like gun and knife detection. There is much research going on in the industry about video surveillance, among them. The role of CCTV video has been overgrown, and CCTV cameras are placed all over the place for surveillance and security. The user gets notified for detecting the objects. It is crucial to proper surveillance for the safety and security of people and their assets. The libraries which have been used for detecting the object are TensorFlow, OpenCV, etc. The Convolutional Neural Network (CNN) is a deep learning algorithm that can take in an input image, assign importance to various aspects and objects in the image and be able to differentiate one from the other. The typical applications of deep surveillance are theft identification, violence detection, and detection of the chances of explosion.

Keywords:

Theft identification, Theft detection, Data processing, Object recognition, Security protocols.

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