Video-based sensor networks can provide important visual information in a number of applications including: environmental monitoring, health care, emergency response, and video security. This article describes the Panoptes video-based sensor networking architecture, including its design, implementation, and performance. We describe two video sensor platforms that can deliver high-quality video over 802.11 networks with a power requirement less than 5 watts. In addition, we describe the streaming and prioritization mechanisms that we have designed to allow it to survive long-periods of disconnected operation. Finally, we describe a sample application and bitmapping algorithm that we have implemented to show the usefulness of our platform. Our experiments include an in-depth analysis of the bottlenecks within the system as well as power measurements for the various components of the system.

We describe an approach to 3D multimodal interaction in immersive augmented and virtual reality environments that accounts for the uncertain nature of the information sources. The resulting multimodal system fuses symbolic and statistical information from a set of 3D gesture, spoken language, and referential agents. The referential agents employ visible or invisible volumes that can be attached to 3D trackers in the environment, and which use a timestamped history of the objects that intersect them to derive statistics for ranking potential referents. We discuss the means by which the system supports mutual disambiguation of these modalities and information sources, and show through a user study how mutual disambiguation accounts for over 45% of the successful 3D multimodal interpretations. An accompanying video demonstrates the system in action.

We describe an approach to 3D multimodal interaction in immersive augmented and virtual reality environments that accounts for the uncertain nature of the information sources. The resulting multimodal system fuses symbolic and statistical information from a set of 3D gesture, spoken language, and referential agents. The referential agents employ visible or invisible volumes that can be attached to 3D trackers in the environment, and which use a time-stamped history of the objects that intersect them to derive statistics for ranking potential referents. We discuss the means by which the system supports mutual disambiguation of these modalities and information sources, and show through a user study how mutual disambiguation accounts for over 45% of the successful 3D multimodal interpretations. An accompanying video demonstrates the system in action.

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A problem faced by groups that are not co-located but need to collaborate on a common task is the reduced access to the rich multimodal communicative context that they would have access to if they were collaborating face-to-face. Collaboration support tools aim to reduce the adverse effects of this restricted access to the fluid intermixing of speech, gesturing, writing and sketching by providing mechanisms to enhance the awareness of distributed participants of each others' actions. In this work we explore novel ways to leverage the capabilities of multimodal context-aware systems to bridge colocated and distributed collaboration contexts. We describe a system that allows participants at remote sites to collaborate in building a project schedule via sketching on multiple distributed whiteboards, and show how participants can be made aware of naturally occurring pointing gestures that reference diagram constituents as they are performed by remote participants. The system explores the multimodal fusion of pen, speech and 3D gestures, coupled to the dynamic construction of a semantic representation of the interaction, anchored on the sketched diagram, to provide feedback that overcomes some of the intrinsic ambiguities of pointing gestures.

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New language constantly emerges from complex, collaborative human-human interactions like meetings -such as, for instance, when a presenter handwrites a new term on a whiteboard while saying it. Fixed vocabulary recognizers fail on such new terms, which often are critical to dialogue understanding. We present a proof-of-concept multimodal system that combines information from handwriting and speech recognition to learn the spelling, pronunciation and semantics of out-of-vocabulary terms from single instances of redundant multimodal presentation (e.g. saying a term while handwriting it). For the task of recognizing the spelling and semantics of abbreviated Gantt chart labels across a held-out test series of five scheduling meetings we show a significant relative error rate reduction of 37% when our learning methods are used and allowed to persist across the meeting series, as opposed to when they are not used.

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