As statistics have shown, forty-two percent of all injury accidents in Germany’s road traffic happen at intersections. Infrastructure-mounted cameras for traffic analysis have been proposed to reduce this number as well as simulation tools, which assist in developing Car-to-Infrastructure (C2I) communication applications in the field of driver assistance, pedestrian, vehicle and traffic safety by a combination of a real application and virtual scenarios. This paper describes an infrastructure-based vision system for pedestrian and vehicle detection, its integration in the C2X-communication software development framework viilab and the visualisation to display the acquired data in a C2X-vehicle. Two cameras are used to monitor an intersection in the visible spectral range out of different views. With methods of computer vision and machine learning road users are detected and analysed as pedestrians or vehicles for both views. The merged objects’ positions are transformed into world coordinates and tracks within the traffic trace are generated. The data can be used in a simulation or can be requested in real time from C2X enabled cars via a roadside unit (RSU) as an environment radar. The performance of the system is discussed.