Surveillance systems are very important to prevent situations where armed people appear. To minimize human supervision, there are algorithms based on artificial intelligence that perform a large part of the identification and detection tasks. These systems usually require large data processing servers. However, a high number of cameras causes congestion in the networks due to a large amount of data being sent. This work introduces a novel system for identifying individuals with weapons by leveraging Edge, Fog, and Cloud computing. The key advantages include minimizing the data transmitted to the Cloud and optimizing the computations performed within it. The main benefits of our proposal are the high and simple scalability, the immediacy of the detection, as well as the optimization of processes through distributed processing of high performance in the Fog layer. Moreover, the structure of this proposal is suitable for 5G camera networks, which require low latency and quick responses.

中文翻译：

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用于武器检测和人脸识别的分布式雾计算系统


监控系统对于防止出现武装人员的情况非常重要。为了最大限度地减少人工监督，有一些基于人工智能的算法可以执行大部分识别和检测任务。这些系统通常需要大型数据处理服务器。但是，由于发送了大量数据，大量摄像机会导致网络拥塞。这项工作介绍了一种利用 Edge、Fog 和云计算来识别持有武器的个体的新型系统。主要优势包括最大限度地减少传输到云的数据并优化在其中执行的计算。我们提案的主要好处是高且简单的可扩展性、检测的即时性，以及通过在雾层中对高性能进行分布式处理来优化流程。此外，该提案的结构适用于需要低延迟和快速响应的 5G 摄像头网络。