Bringing Richer Information with Reliability to Automated Traffic Monitoring from the Fusion of Multiple Cameras, Inductive Loops and Road Maps

This paper presents a novel, deterministic framework toextract the traffic state of an intersection with high reliabilityand in real-time. The multiple video cameras and inductiveloops at the intersection are fused on a common planewhich consists of a satellite map. The sensors are registeredfrom a CAD map of the intersection that is aligned on thesatellite map. The cameras are calibrated to provide themapping equations that project the detected vehicle positionsonto the coordinate system of the satellite map. Weuse a night time vehicle detection algorithm to process thecamera frames. The inductive loops confirm or reject thevehicle tracks measured by the cameras, and the fusion ofcamera and loop provides an additional feature : the vehiclelength. A Kalman filter linearly tracks the vehicles alongthe lanes. Over time, this filter reduces the noise presentin the measurements. The advantage of this approach isthat the detected vehicles and their parameters acquire avery high confidence, which brings almost 100% accuracyof the traffic state. An empirical evaluation is performedon a testbed intersection. We show the improvement of thisframework over single sensor frameworks.