Visual tracking for intelligent vehicle-highway systems

The complexity and congestion of current transportation systems often produce traffic situations that jeopardize the safety of the people involved, These situations vary from maintaining a safe distance behind a leading vehicle to safely allowing a pedestrian to cross a busy street. Environmental sensing plays a critical role in virtually all of these situations, Of the sensors available, vision sensors protide information that Is richer and more complete than other sensors, making them a logical choice for a multisensor transportation system, In this paper we propose robust detection and tracking techniques for intelligent vehicle-highway applications where computer vision plays a crucial role, In particular, se demonstrate that the Controlled Active Vision framework [15] can be utilized to provide a visual tracking modality to a traffic advisory system in order to increase the overall safety margin in a variety bf common traffic situations, We have selected two application examples. vehicle tracking and pedestrian tracking, to demonstrate that the framework fan provide precisely the type of information required to effectively manage the given traffic situation.