TrafficEd: Deployment and Management System of Edge AI Cameras

Artificial intelligence (AI) cameras are edge devices with embedded graphics processing units that can run lightweight deep learning models. In traffic management applications, traffic flow and traffic incidents can be detected from roadside images with the use of AI cameras, and only detected high-level information is sent to the server to minimize the use of network bandwidth and server resources. However, because edge devices are computationally limited, models should be optimized before they are deployed to these AI cameras. In addition, environment-related parameters must be configured appropriately after model deployment. Thus, an AI camera management system is required. Consequently, in this study, we designed a deployment and management system for AI cameras; this system can perform model optimization and parameter configuration with ease. The main functions of this system involve 1) automatic modeling and code transfer, 2) the remote deployment of deep learning models, 3) the remote configuration of relevant applications, and 4) the presentation of analytical results on a graphical user interface. The performance of the developed system was investigated by using it to deploy traffic analysis models and visualize analysis results. The experimental results indicate that this system achieved all of its design goals.