DEEP LEARNING APPROACH FOR EVENT MONITORING SYSTEM

<p>DEEP LEARNING APPROACH FOR EVENT MONITORING SYSTEM</p> <p><br>Kummari Vikas, Thipparthi Rajabrahmam, Ponnam Venu, and Shanmugasundaram Hariharan<br>Department of CSE, Vardhaman College of Engineering, Hyderabad, India</p> <p><br>ABSTRACT</p> <p><br>With an increasing number of extreme events and complexity, more alarms are being used to monitor<br>control rooms. Operators in the control rooms need to monitor and analyze these alarms to take suitable<br>actions to ensure the system’s stability and security. Security is the biggest concern in the modern world. It<br>is important to have a rigid surveillance that should guarantee protection from any sought of hazard.<br>Considering security, Closed Circuit TV (CCTV) cameras are being utilized for reconnaissance, but these<br>CCTV cameras require a person for supervision. As a human being, there can be a possibility to be tired<br>off in supervision at any point of time. So, we need a system to detect automatically. Thus, we came up with<br>a solution using YOLO V5. We have taken a data set and used robo-flow framework to enhance the existing<br>images into numerous variations where it will create a copy of grey scale image, a copy of its rotation and<br>a copy of its blurred version which will be used to get an enlarged data set. This work mainly focuses on<br>providing a secure environment using CCTV live footage as a source to detect the weapons. Using YOLO<br>algorithm, it divides an image from the video into grid system and each grid detects an object within itself.</p> <p>KEYWORDS</p> <p><br>Convolutional Neural network (CNN), YOLO algorithm, Kaggle, Support vector machine (SVM). </p> <p>Pdf Link: https://aircconline.com/ijmit/V14N3/14322ijmit02.pdf</p> <p>Volume Link: https://airccse.org/journal/ijmit/vol14.html</p> <p>Other Details: https://airccse.org/journal/ijmit/ijmit.html</p>