Author:
T.Krishna Prasath, Hareesh, Mohamed Pahad
Published in
Journal of Science Technology and Research
( Volume , Issue )
Page No: 81-90
Volume , Issue
Article Type:
Published Date:
Published by:
Abstract
Anamoly detection in videos plays an important role in various real-life applications. Most of traditional approaches depend on utilizing handcrafted features which are problem-dependent and optimal for specific tasks. Nowadays, there has been a rise in the amount of disruptive and offensive activities that have been happening. Due to this, security has been given principal significance. Public places like shopping centers, avenues, banks, etc. are increasingly being equipped with CCTVs to guarantee the security of individuals. Subsequently, this inconvenience is making a need to computerize this system with high accuracy. Since constant observation of these surveillance cameras by humans is a near-impossible task. It requires workforces and their constant attention to judge if the captured activities are anomalous or suspicious. Hence, this drawback is creating a need to automate this process with high accuracy. Moreover, there is a need to display which frame and which parts of the recording contain the uncommon activity which helps the quicker judgment of that unordinary action being unusual or suspicious. Therefore, to reduce the wastage of time and labour, we are utilizing deep learning algorithms for Automating Threat Recognition System. Its goal is to automatically identify signs of aggression and violence in real-time, which filters out irregularities from normal patterns. We intend to utilize different Deep Learning models (CNN and RNN) to identify and classify levels of high movement in the frame. From there, we can raise a detection alert for the situation of a threat, indicating the suspicious activities at an instance of time and spray the smoke spray.
Keywords
Internet of Things, Image Processing, Cloud Access, Security System.
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ABSTRACT:

The IoT-Based Intruder Prevention system using Fogger provides an intelligent and automated approach to enhancing public security. Traditional surveillance relies heavily on manual monitoring, which is time-consuming and often inaccurate. To address this, our system uses deep learning models like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to detect anomalies in real-time video footage. Once suspicious activity is identified, the system instantly triggers a fogger to obscure the intruder’s vision and sends an alert to the security team. This dual response minimizes human intervention and accelerates the threat response time. This IoT Based Intruder Prevention system highlights specific frames and areas containing abnormal behavior, making it easier for authorities to assess threats. Moreover, the integration of IoT technology enables remote monitoring and control, increasing flexibility and reliability. Designed for high-risk environments such as banks, malls, and offices, this system ensures improved safety, reduced workload for personnel, and faster.

INTRODUCTION:

Security has become a growing concern due to the increasing number of disruptive and offensive activities. Although CCTV surveillance is widely used in public and private spaces, constant human monitoring is nearly impossible and highly inefficient. To overcome this limitation, we developed an automated system that uses deep learning and IoT to detect suspicious behavior in real-time. Our approach eliminates the need for continuous manual supervision by intelligently identifying anomalies in video frames. Using CNN for feature extraction and RNN with LSTM for sequence prediction, the system accurately detects abnormal movements. Once a threat is identified, it immediately activates a fogger to obscure visibility and prevent further intrusion. Additionally, it alerts security personnel with specific timestamps and frame highlights. By integrating automation and machine learning, the system enhances the efficiency of surveillance operations and ensures improved public safety across locations like banks, malls, and institutions.

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