Many interesting and promising prototypes for visualizing video data have been proposed, including those that combine videos with their spatial context (contextualized videos). However, relatively little work has investigated the fundamental design factors behind these prototypes in order to provide general design guidance. Focusing on real-time video data visualization, we evaluated two important design factors--video placement method and spatial context presentation method--through a user study. In addition, we evaluated the effect of spatial knowledge of the environment. Participants' performance was measured through path reconstruction tasks, where the participants followed a target through simulated surveillance videos and marked the target paths on the environment model. We found that embedding videos inside the model enabled realtime strategies and led to faster performance. With the help of contextualized videos, participants not familiar with the real environment achieved similar task performance to participants that worked in that environment. We discuss design implications and provide general design recommendations for traffic and security surveillance system interfaces.

Multiple spatially-related videos are increasingly used in security, communication, and other applications. Since it can be difficult to understand the spatial relationships between multiple videos in complex environments (e.g. to predict a person's path through a building), some visualization techniques, such as video texture projection, have been used to aid spatial understanding. In this paper, we identify and begin to characterize an overall class of visualization techniques that combine video with 3D spatial context. This set of techniques, which we call contextualized videos, forms a design palette which must be well understood so that designers can select and use appropriate techniques that address the requirements of particular spatial video tasks. In this paper, we first identify user tasks in video surveillance that are likely to benefit from contextualized videos and discuss the video, model, and navigation related dimensions of the contextualized video design space. We then describe our contextualized video testbed which allows us to explore this design space and compose various video visualizations for evaluation. Finally, we describe the results of our process to identify promising design patterns through user selection of visualization features from the design space, followed by user interviews.

A significant problem encountered when building Augmented Reality (AR) systems is that all spatial knowledge about the world has uncertainty associated with it. This uncertainty manifests itself as registration errors between the graphics and the physical world, and ambiguity in user interaction. In this paper, we show how estimates of the registration error can be leveraged to support predictable selection in the presence of uncertain 3D knowledge. These ideas are demonstrated in osgAR, an extension to OpenSceneGraph with explicit support for uncertainty in the 3D transformations. The osgAR runtime propagates this uncertainty throughout the scene graph to compute robust estimates of the probable location of all entities in the system from the user's viewpoint, in real-time. We discuss the implementation of selection in osgAR, and the issues that must be addressed when creating interaction techniques in such a system.

An important problem for augmented reality is registration error. No system can be perfectly tracked, calibrated or modeled. As a result, the overlaid graphics are not aligned perfectly with objects in the physical world. This can be distracting, annoying or confusing. In this paper, we propose a method for mitigating the effects of registration errors that enables application developers to build dynamically adaptive AR displays. Our solution is implemented in a programming toolkit called OSGAR. Built upon OpenSceneGraph (OSG), OSGAR statistically characterizes registration errors, monitors those errors and, when a set of criteria are met, dynamically adapts the display to mitigate the effects of the errors. Because the architecture is based on a scene graph, it provides a simple, familiar and intuitive environment for application developers. We describe the components of OSGAR, discuss how several proposed methods for error registration can be implemented, and illustrate its use through a set of examples.

All augmented reality (AR) systems must deal with registration errors. While most AR systems attempt to minimize registration errors through careful calibration, registration errors can never be completely eliminated in any realistic system. In this paper, we describe a robust and efficient statistical method for estimating registration errors. Our method generates probabilistic error estimates for points in the world, in either 3D world coordinates or 2D screen coordinates. We present a number of examples illustrating how registration error estimates can be used in AR interfaces, and describe a method for estimating registration errors of objects based on the expansion and contraction of their 2D convex hulls.