Sensor networks and associated infrastructures become ever more important to the traffic monitoring and control because of the increasing traffic demands in terms of congestion and safety. These systems allow authorities not only to monitor the traffic state at the detection sites, but also to obtain real-time related information (e.g. traffic loads). This study presents a real-time vision system for automatic traffic monitoring based on a network of autonomous tracking units (ATUs) that capture and process images from one or more pre-calibrated cameras. The proposed system is flexible, scalable and suitable for a broad field of applications, including traffic monitoring of tunnels at highways and aircraft parking areas at airports. Another objective of this work is to test and evaluate different image processing and data fusion techniques in order to be incorporated to the final system. The output of the image processing unit is a set of information for each moving object in the scene, such as target ID, position, velocity and classification, which are transmitted to a remote traffic control centre, with remarkably low bandwidth requirements. This information is analysed and used to provide real-time output (e.g. alerts, electronic road signs, ramp meters etc.) as well as to extract useful statistical information (traffic loads, lane changes, average velocity etc.).