Augmented reality deals with the problem of dynamically augmenting or enhancing (images or live video of) the real world with computer generated data (e.g., graphics of virtual objects). This poses two major problems: (a) determining the precise alignment of real and virtual coordinate frames for overlay, and (b) capturing the 3D environment including camera and object motions. The latter is important for interactive augmented reality applications where users can interact with both real and virtual objects. Here we address the problem of accurately tracking the 3D motion of a monocular camera in a known 3D environment and dynamically estimating the 3D camera location. We utilize fully automated landmark-based camera calibration to initialize the motion estimation and employ extended Kalman filter techniques to track landmarks and to estimate the camera location. The implementation of our approach has been proven to be efficient and robust and our system successfully tracks in real-tim...

This paper describes two projects applying computer vision to Intelligent Vehicle Highway Systems. The first project has resulted in the development of a system for monitoring traffic scenes using video information. The objective is to estimate traffic parameters such as flow rates, speeds and link travel times, as well as to detect quickly disruptive incidents such as stalled vehicles and accidents. The second project is aimed at developing vision as a sensor technology for vehicle control. The novel feature of this project, compared to most previous approaches, is the extensive use of binocular stereopsis. First, it provides information for obstacle detection, grouping, and range estimation which is directly used for longitudinal control. Secondly, the obstacle-ground separation enables robust localization of partially occluded lane boundaries as v/ell as the dynamic update of camera rig parameters to deal with vibrations and vertical road curvature. 1

Images of vehicles which move in traffic scenes recorded by a stationary camera have been detected and tracked without operator intervention. The resulting vehicle trajectories were projected from the image plane onto the street plane. A suitable system internal representation of about 90 German motion verbs was then exploited in order to automatically characterize trajectory segments in terms of natural language concepts. A multiresolution approach for feature matching has been developed which is robust enough to track vehicle images across hundreds of frames, despite considerable variations in size and projected velocity. Results from experiments with image sequences from real-world traffic scenes are presented. >