A Video Surveillance System (VSS) is of primary importance in public transport hubs such as airports, train or bus stations but also inside the vehicle itself. In this paper, we present a heuristic architecture model for on-board video surveillance system based on Internet of Video Things (IoVT) devices which addresses the need for delivering smart video surveillance in public transport vehicles (e.g., buses) minimizing the impact on network performance. A proof-of-concept was implemented using a public Cloud Service Provider (CSP) and two Raspberry Pi as edge computing nodes. On the edge nodes, a Machine Learning(ML) application was deployed along with a network-efficient video streaming system. On the other hand, laboratory tests are included to understand the network traffic dynamic, furthermore results are enhanced with a set of simulations in order to analyze the performance of a video streaming application and6differentcongestion control algorithms in terms of packet loss and delay.

中文翻译：

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一种用于公共交通车辆视频监控的边缘云方法

视频监控系统 (VSS) 在机场、火车站或公交车站等公共交通枢纽以及车辆内部都非常重要。在本文中，我们提出了一种基于视频物联网 (IoVT) 设备的车载视频监控系统的启发式架构模型，该模型满足了在公共交通车辆（例如公交车）中提供智能视频监控的需求，最大限度地减少对网络的影响表现。使用公共云服务提供商 (CSP) 和两个 Raspberry Pi 作为边缘计算节点实现了概念验证。在边缘节点上，部署了机器学习 (ML) 应用程序以及网络高效的视频流系统。另一方面，包括实验室测试以了解网络流量动态，