Traffic surveillance cameras are the eyes of the Intelligent Transportation Systems (ITS). However, they are currently isolated and can only extract information from each of their fixed views. To track vehicles across multiple cameras and help public agencies collect link travel time and speed information, an Edge-empowered Cooperative Multi-camera Sensing (ECoMS) System is proposed. ECoMS system presents a novel algorithmic and edge-server cooperative system construct to push edge computing and multi-camera re-identification workflow serving for traffic sensing based on Internet of Things (IoT) architecture. On the algorithm side, ECoMS system proposes a featherlight edge-based computer vision framework for vehicle detection, tracking, and features selection process in a real-time manner. Then, by only sending the objects’ representations to the server, the high-bandwidth data transmission and the heavy post-processing system can be abandoned. Furthermore, a hierarchical clip-based deep vehicle re-identification framework is proposed and integrated into the ECoMS system, and significantly outperforms other state-of-the-art methods by 4%–8% on Rank-1 accuracy. Finally, to balance the accuracy level of different camera pairs, a collaborative cross-camera traffic information estimation framework based on kernel density estimation with kernel smoother is implemented, which can get the precise link and region traffic information together with distributions (less than 1.01 KL distance). By maximizing the cooperation of the computational resources, orchestrating the data transmission and integrating road network graph features in the system, the ECoMS can precisely model the network-scale traffic information in a flexible, cost-effective, and scalable workflow. To the author’s best knowledge, ECoMS is the first multi-camera vehicle tracking and traffic monitoring system based on cooperative IoT architecture.

交通监控摄像头是智能交通系统（ITS）的眼睛。然而，他们目前是孤立的，只能从他们各自的固定视图中提取信息。为了通过多个摄像头跟踪车辆并帮助公共机构收集链路行程时间和速度信息，提出了一种边缘赋能的协作多摄像头传感 (ECoMS) 系统。ECoMS 系统提出了一种新颖的算法和边缘服务器协作系统构造，以推动边缘计算和多摄像头重新识别工作流服务于基于物联网 (IoT) 架构的交通传感。在算法方面，ECoMS 系统提出了一种基于 featherlight 边缘的计算机视觉框架，用于实时进行车辆检测、跟踪和特征选择过程。然后，通过仅将对象的表示发送到服务器，可以放弃高带宽数据传输和繁重的后处理系统。此外，提出了一种基于分层剪辑的深度车辆重新识别框架并将其集成到 ECoMS 系统中，在 Rank-1 准确度上显着优于其他最先进的方法 4%–8%。最后，为了平衡不同相机对的精度水平，实现了基于内核平滑器的核密度估计的协同跨相机交通信息估计框架，可以获得精确的链路和区域交通信息以及分布（小于1.01 KL距离）。通过最大化计算资源的协作，编排数据传输并在系统中集成路网图特征，ECoMS 可以在灵活、经济高效且可扩展的工作流程中对网络规模的交通信息进行精确建模。据作者所知，ECoMS 是第一个基于协作物联网架构的多摄像头车辆跟踪和交通监控系统。