Video Object Detection for Police Surveillance using Deep Learning

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Abstract

Human vision is incredibly excellent and complex. In the previous years, people made significantly more leaps to expanding this visual capacity to machines. Cameras have been used as the eyes of computers.In response to increasing anxieties about crime and its threat to security and safety, the utilization of substantial numbers of closed-circuit television system (CCTV) in both public and private spaces have been considered a necessity. The use of these significant video footages is essential to incident investigations.But as the number of these systems rises, so as the need for human operator monitoring tasks.Unfortunately, many actionable incidents are utterly undetected in this manual systemdue to inherent limitations from deploying solely human operators eye-balling CCTV screens.As a result, surveillance footages are often used merely as passive records or as evidence for post-event investigations. This study aimed to develop a real-time firearm detection using deep learning embedded in CCTV cameras that pushes alert notifications to both iOS and Android mobile devices.This research used a descriptive design and asked IT experts to evaluate the develop system based on its compliance to ISO 25010 standard. Moreover, confusion matrix and intersection over union (IoU) were used to evaluate the performanceof the system.The detection system was found to be highly recommended in urban areas particularly for CCTVs found in barangay streets and establishments.
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基于深度学习的警察监控视频目标检测
人类的视觉非常优秀和复杂。在过去的几年里,人们在将这种视觉能力扩展到机器方面取得了显著的飞跃。照相机被用作电脑的眼睛。为了应对日益增加的对犯罪及其对安全的威胁的担忧,在公共和私人空间大量使用闭路电视系统(CCTV)被认为是必要的。使用这些重要的录像片段对事件调查至关重要。但随着这些系统数量的增加,对人工操作员监控任务的需求也随之增加。不幸的是,由于仅仅部署人工操作员盯着闭路电视屏幕的固有限制,许多可操作的事件在这个人工系统中完全没有被发现。因此,监控录像往往只被用作被动记录或事后调查的证据。这项研究旨在开发一种实时枪支检测技术,利用嵌入在闭路电视摄像机中的深度学习技术,向iOS和Android移动设备推送警报通知。本研究使用描述性设计,并要求IT专家根据其对ISO 25010标准的遵从性来评估开发系统。此外,还使用混淆矩阵和交联(IoU)来评价系统的性能。强烈建议在城市地区使用该检测系统,特别是在乡村街道和场所发现的闭路电视。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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