Deep Learning Approach for Event Monitoring System

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Deep Learning Approach for Event Monitoring System

Authors

Kummari Vikas, Thipparthi Rajabrahmam, Ponnam Venu,

and Shanmugasundaram Hariharan

Department of CSE, Vardhaman College of Engineering, Hyderabad, India

Abstract  

With an increasing number of extreme events and complexity, more alarms are being used to monitor control rooms. Operators in the control rooms need to monitor and analyze these alarms to take suitable actions to ensure the system’s stability and security. Security is the biggest concern in the modern world. It is important to have a rigid surveillance that should guarantee protection from any sought of hazard. Considering security, Closed Circuit TV (CCTV) cameras are being utilized for reconnaissance, but these CCTV cameras require a person for supervision. As a human being, there can be a possibility to be tired off in supervision at any point of time. So, we need a system to detect automatically. Thus, we came up with a solution using YOLO V5. We have taken a data set and used robo-flow framework to enhance the existing images into numerous variations where it will create a copy of grey scale image, a copy of its rotation and a copy of its blurred version which will be used to get an enlarged data set. This work mainly focuses on providing a secure environment using CCTV live footage as a source to detect the weapons. Using YOLO algorithm, it divides an image from the video into grid system and each grid detects an object within itself.

Keywords

Convolutional Neural network (CNN), YOLO algorithm, Kaggle, Support vector machine (SVM). 

Paper URL

https://aircconline.com/ijmit/V14N3/14322ijmit02.pdf

Volume URL:

https://airccse.org/journal/ijmit/vol14.html

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International Journal of Managing Information Technology (IJMIT) -h-index 29

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https://airccse.org/journal/ijmit/ijmit.html

WJCI Impact Factor : 0.040

ISSN: 0975-5586 (Online); 0975-5926 (Print)

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