Heimdall：基于AI的流量监控和异常检测基础设施
Heimdall: an AI-based infrastructure for traffic monitoring and anomalies detection
摘要：
自出现以来，智慧城市的目标是改善人们的日常生活，帮助使公共服务更智能，更高效。其中一些服务往往旨在为公民和司机提供更好的安全条件。在这方面，我们提出了Heimdall，一个基于AI的视频监控系统，用于交通监控和异常检测。拟议的系统具有三个主要层次：地面层，由一组配备摄像头和传感器的智能灯柱组成，以及用于实时检测事故和交通异常的先进人工智能单元;区域层，集成和组合从不同灯柱收集的信息，并将其与外部数据源交叉关联，以协调和处理警告和警报;一个培训级别，负责不断提高必须感知环境的模块的准确性。最后，我们提出并讨论了一种基于Faster R-CNN的早期异常检测实验方法，并在拟议的基础设施中采用。
英文摘要：
Since their appearance, Smart Cities have aimed at improving the daily life of people, helping to make public services smarter and more efficient. Several of these services are often intended to provide better security conditions for citizens and drivers. In this vein, we present Heimdall, an AI-based video surveillance system for traffic monitoring and anomalies detection. The proposed system features three main tiers: a ground level, consisting of a set of smart lampposts equipped with cameras and sensors, and an advanced AI unit for detecting accidents and traffic anomalies in real time; a territorial level, which integrates and combines the information collected from the different lampposts, and cross-correlates it with external data sources, in order to coordinate and handle warnings and alerts; a training level, in charge of continuously improving the accuracy of the modules that have to sense the environment. Finally, we propose and discuss an early experimental approach for the detection of anomalies, based on a Faster R-CNN, and adopted in the proposed infrastructure.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
