Abstract.Environmental monitoring applications present a challenge to current background subtraction algorithms that analyze the temporal variability of pixel intensities, due to the complex texture and motion of the scene. They also present a challenge to segmentation algorithms that compare intensity or color distributions between the foreground and the background in each image independently, because objects of interest such as animals have adapted to blend in. Therefore, we have developed a background modeling and subtraction scheme that analyzes the temporal variation of intensity or color distributions, instead of either looking at temporal variation of point statistics, or the spatial variation of region statistics in isolation. Distributional signatures are less sensitive to movements of the textured background, and at the same time they are more robust than individual pixel statistics in detecting foreground objects. They also enable slow background update, which is crucial in monitoring applications where processing power comes at a premium, and where foreground objects, when present, may move less than the background and therefore disappear into it when a fast update scheme is used. Our approach compares favorably with the state of the art both in generic lowlevel detection metrics, as well as in application-dependent criteria.

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Humans use a combination of gesture and speech to interact with objects and usually do so more naturally without holding a device or pointer. We present a system that incorporates user body-pose estimation, gesture recognition and speech recognition for interaction in virtual reality environments. We describe a vision-based method for tracking the pose of a user in real time and introduce a technique that provides parameterized gesture recognition. More precisely, we train a support vector classifier to model the boundary of the space of possible gestures, and train Hidden Markov Models on specific gestures. Given a sequence, we can find the start and end of various gestures using a support vector classifier, and find gesture likelihoods and parameters with a HMM. A multimodal recognition process is performed using rank-order fusion to merge speech and vision hypotheses. Finally we describe the use of our multimodal framework in a virtual world application that allows users to interact using gestures and speech.

After 911 1, the United States (U.S.) was suddenly pushed into challenging situations they could no longer ignore as simple spectators. The War on Terrorism (WoT) was suddenly ignited and no one knows when this war will end. While the government is exploring many existing and potential technologies, the area of irel less Sensor networks (WSN) has emerged as a foundation for establish future national security. Unlike other technologies, WSN could provide virtual presence capabilities needed for precision awareness and response in military, intelligence, and homeland security applications. The Advance Concept Group (ACG) vision of Sense/Decide/Act/Communicate (SDAC) sensor system is an instantiation of the WSN concevt that takes a "systems of systems";iew. Each sensing nodes will exhibit the ability to: Sense the environment around them, Decide as a collective what the situation of their environment is, Act in an intelligent and coordinated manner in response to this situational determination, and Communicate their actions amongst each other and to a human command. This LDRD report -provides a review of the research and development done to bring the SDAC vision closer to d reality.

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