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The authors discuss computing systems using holistic sensing and dynamic active displays, embedded in vehicles to improve driver safety. They cite 2006 research findings that driver inattention is to blame for most crashes and near crashes. The most effective intelligent driver support systems will be based on an understanding of both general driving habits and the individual vehicle operator's own driving behavior. The systems must also be introduced in such a way as to avoid distracting or confusing the driver.

Research presented in this paper deals with the systematic examination, development, and evaluation of a novel multimodal registration approach that can perform accurately and robustly for relatively close range surveillance applications. An analysis of multimodal image registration gives insight into the limitations of assumptions made in current approaches and motivates the methodology of the developed algorithm. Using calibrated stereo imagery, we employ maximization of mutual information in sliding correspondence windows that inform a disparity voting algorithm to demonstrate successful registration of objects in color and thermal imagery. Extensive evaluation of scenes with multiple objects at different depths and levels of occlusion shows high rates of successful registration. Ground truth experiments demonstrate the utility of the disparity voting techniques for multimodal registration by yielding qualitative and quantitative results that outperform approaches that do not consider occlusions. A basic framework for multimodal stereo tracking is investigated and promising experimental studies show the viability of using registration disparity estimates as a tracking feature. [Copyright &y& Elsevier]

This paper presents a novel approach to recognizing driver activities using a multi-perspective (i.e., four camera views) multi-modal (i.e., thermal infrared and color) video-based system for robust and real-time tracking of important body parts. The multi-perspective characteristics of the system provides redundant trajectories of the body parts, while the multi-modal characteristics of the system provides robustness and reliability of feature detection and tracking. The combination of a deterministic activity grammar (called ‘operation-triplet’) and a Hidden Markov model-based classifier provides semantic-level analysis of human activity. The application context for this research is that of intelligent vehicles and driver assistance systems. Experimental results in real-world street driving demonstrate effectiveness of the proposed system. [Copyright &y& Elsevier]

The article discusses the development of the video surveillance approach, Distributed Interactive Video Array, that employs a large-scale cluster of video sensors and demonstrates the promise of multicamera arrays for homeland security. Such systems offer several advantages over single camera systems by providing multiple overlapping views for obtaining three-dimensional information and handling occlusions, multiple nonoverlapping cameras for covering wide areas and active pan-tilt-zoom cameras for observing object details.

In this paper we present a set of novel methods for image-based modeling using omnidirectional vision sensors. The basic idea is to directly and efficiently acquire plenoptic representations by using omnidirectional vision sensors. The three methods, in order of increasing complexity, are direct memorization, discrete interpolation, and smooth interpolation. Results of these methods are compared visually with ground-truth images taken from a standard camera walking along the same path. The experimental results demonstrate that our methods are successful at generating high-quality virtual images. In particular, the smooth interpolation technique approximates the plenoptic function most closely. A comparative analysis of the computational costs associated with the three methods is also presented. [ABSTRACT FROM AUTHOR]

Discusses the identification of human movement capture and analysis in intelligent environments. Evaluation of the video and audio signals in real time for low-level tasks; Intrusion on the normal human activities in the space, all sensors, processors and communication devices; Integration of sensory data processing and databases.