Deep Learning Approach for Event Monitoring System

Kummari Vikas, Thipparthi Rajabrahmam, Ponnam Venu, S. Hariharan
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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.
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事件监控系统的深度学习方法
随着极端事件数量和复杂性的增加,越来越多的警报被用于监控控制室。控制室的操作人员需要对这些告警进行监控和分析,以便采取适当的措施,以确保系统的稳定和安全。安全是当今世界最大的问题。重要的是要有严格的监督,以确保免受任何危险的侵害。考虑到安全性,目前正在使用闭路电视(CCTV)摄像机进行监视,但这些摄像机需要一个人监督。作为一个人,在任何时候都有可能在监督中感到疲惫。所以,我们需要一个系统来自动检测。因此,我们提出了使用YOLO V5的解决方案。我们取了一个数据集,并使用机器人流框架将现有图像增强为许多变体,其中它将创建一个灰度图像的副本,一个旋转图像的副本和一个模糊版本的副本,这将用于获得扩大的数据集。这项工作主要集中在提供一个安全的环境,使用闭路电视实况录像作为来源,以发现武器。该算法采用YOLO算法,将视频图像划分为网格系统,每个网格检测其内部的目标。
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