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首页> 外文期刊>Quality Control, Transactions >An Inexpensive Upgradation of Legacy Cameras Using Software and Hardware Architecture for Monitoring and Tracking of Live Threats
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An Inexpensive Upgradation of Legacy Cameras Using Software and Hardware Architecture for Monitoring and Tracking of Live Threats

机译：使用软件和硬件架构进行遗留升级的遗产升级，用于监控和跟踪现场威胁

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Surveillance through digital cameras is increasing exponentially. A majority of these cameras are not smart cameras; therefore, they send their video stream to a central server where it is processed and analyzed for any threats. Typically, human operators or machine learning algorithms at the cloud analyzed and processed the post-event videos to track and locate the perpetrator or victim. The centralized approach leads to two primary shortcomings: 1) the high cost of cloud infrastructure; 2) lack of instant tracking and detection of the threat. One solution is to replace these legacy cameras with the smart cameras so they can process information locally. Although the solution is costly, it could solve the real-time threat detection issues. However, the need for a central server remains there, to construct the path of threat, when threat moves from one camera view to another. The existing distributed architectures for threat tracking shifts the load of threat capturing and processing from a central server to the edge nodes, which in turn reduces the computational power but does not remove the role of the central server completely. These architectures don & x2019;t equip each camera of processing and communicating with each other. Further, in the existing distributed architectures, the local cameras are not able to store the path of the threat individually and just transmit the captured trajectory to the central body. This research proposed a second alternative that makes use of legacy cameras through additional hardware and software components such that they can process information and collaborate locally. The research addresses the challenge by introducing a low cost distributed threat tracking framework that allows the single camera to identify the threat and communicate its information to other cameras without involving the central server. The framework stores the information in a lightweight architecture that is inspired by the blockchain storage algorithm. The system also allows querying the path traveled by the threat at any stage. To evaluate the system, we performed two simulated experiments: one with a central server and another with the proposed distributed system. The results of the experiments showed that the time to track the threat through the proposed system was lower than the existing centralized system. Moreover, the proposed system predicted the paths of threats with an accuracy of 85.49 & x0025;. In the future, the technique may be improved with reinforcement learning and other machine learning techniques.
机译：通过数码相机监视是指数增长的。大多数这些相机不是智能摄像机;因此，他们将视频流发送到中央服务器，在那里处理和分析任何威胁。通常，云端的人工操作员或机器学习算法分析并处理了事件后视频以跟踪和定位犯罪者或受害者。集中式方法导致两个主要缺点：1）云基础设施的高成本; 2）缺乏即时跟踪和检测威胁。一种解决方案是用智能相机替换这些传统相机，以便它们可以在本地处理信息。虽然解决方案成本高，但它可以解决实时威胁检测问题。但是，当威胁从一个摄像机视图移动到另一个相机视图时，对中央服务器的需要仍然存在，构建威胁的路径。用于威胁跟踪的现有分布式架构将威胁捕获的负载从中央服务器转移到边缘节点，这反过来又降低了计算能力，但不会完全删除中央服务器的角色。这些架构Don＆x2019; t配备了每次加工摄像机和彼此沟通。此外，在现有的分布式架构中，本地摄像机不能单独地存储威胁的路径，并仅将捕获的轨迹传输到中心正文。本研究提出了一种通过额外的硬件和软件组件利用传统摄像机的第二种替代方案，使得它们可以在本地处理信息并在本地协作。该研究通过引入低成本分布式威胁跟踪框架来解决挑战，该概念允许单个相机识别威胁并在不涉及中央服务器的情况下将其信息传送到其他相机。该框架将信息存储在由区块存储算法的启发的轻量级架构中。该系统还允许在任何阶段查询由威胁行进的路径。为了评估系统，我们执行了两个模拟实验：一个带有中央服务器的模拟实验，另一个用拟议的分布式系统。实验结果表明，通过所提出的系统跟踪威胁的时间低于现有的集中系统。此外，所提出的系统预测了威胁的威胁路径，精度为85.49＆x0025;在未来，通过加固学习和其他机器学习技术可以提高该技术。

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