This paper aims at real-time in-car video analysis to detect and track vehicles ahead for safety, autodriving, and target tracing. This paper describes a comprehensive approach to localizing target vehicles in video under various environmental conditions. The extracted geometry features from the video are continuously projected onto a 1-D profile and are constantly tracked. We rely on temporal information of features and their motion behaviors for vehicle identification, which compensates for the complexity in recognizing vehicle shapes, colors, and types. We probabilistically model the motion in the field of view according to the scene characteristic and the vehicle motion model. The hidden Markov model (HMM) is used to separate target vehicles from the background and track them probabilistically. We have investigated videos of day and night on different types of roads, showing that our approach is robust and effective in dealing with changes in environment and illumination and that real-time processing becomes possible for vehicle-borne cameras.

Augmented reality entails the use of models and their associated renderings to supplement information in a real scene. In order for this information to be relevant or meaningful, the models must be positioned and displayed in such a way that they blend into the real world in terms of alignments, perspectives, illuminations, etc. For practical reasons the information necessary to obtain this realistic blending cannot be known a priori, and cannot be hard wired into a system. Instead a number of calibration procedures are necessary so that the location and parameters of each of the system components are known. We identify the calibration steps necessary to build a computer model of the real world and then, using the monitor based augmented reality system developed at ECRC (GRASP) as an example, we describe each of the calibration processes. These processes determine the internal parameters of our imaging devices (scan converter, frame grabber, and video camera), as well as the geometric transformations that relate all of the physical objects of the system to a known world coordinate system.

Augmented reality (AR) is a technology in which a user's view of the real world is enhanced or augmented with additional information generated from a computer model. Using AR technology, users can interact with a combination of real and virtual objects in a natural way. This paradigm constitutes the core of a very promising new technology for many applications. However, before it can be applied successfully, AR has to fulfill very strong requirements including precise calibration, registration and tracking of sensors and objects in the scene, as well as a detailed overall understanding of the scene. We see computer-vision and image-processing technology playing an increasing role in acquiring appropriate sensor and scene models. To balance robustness with automation, we integrate automatic image analysis with both interactive user assistance and input from magnetic trackers and CAD models. Also, in order to meet the requirements of the emerging global information society, future human-computer interaction...