Our long-term objective is to create smart room technologies that are aware of the users presence and their behavior and can become an active, but not an intrusive, part of the interaction. In this work, we present a multimodal approach for estimating and tracking the location and identity of the participants including the active speaker. Our smart room design contains three user-monitoring systems: four CCD cameras, an omnidirectional camera and a 16 channel microphone array. The various sensory modalities are processed both individually and jointly and it is shown that the multimodal approach results in significantly improved performance in spatial localization, identification and speech activity detection of the participants.

An approach for model-free markerless motion capture of articulated kinematic structures is presented. This approach is centered our method for generating underlying nonlinear axes (or a skeleton curve) from the volume of an arbitrary rigid-body model. We describe the use of skeleton curves for deriving a kinematic model and motion (in the form of joint angles over time) from a captured volume sequence. Our motion capture method uses a skeleton curve, found in each frame of a volume sequence, to automatically determine kinematic postures. These postures are then aligned to determine a common kinematic model for the volume sequence. The derived kinematic model is then reapplied to each frame in the volume sequence to find the motion suited to this model. We demonstrate our method for several types of motion from synthetically generated volume sequences with arbitrary kinematic topology and human volume sequences captured from a set of multiple calibrated cameras.