In a known environment, objects may be tracked in multiple views using a set of background models. Stereo-based models can be illumination-invariant, but often have undefined values which inevitably lead to foreground classification errors. We derive dense stereo models for object tracking using long-term, extended dynamic-range imagery, and by detecting and interpolating uniform but unoccluded planar regions. Foreground points are detected quickly in new images using pruned disparity search. We adopt a "latesegmentation" strategy, using an integrated plan-view density representation. Foreground points are segmented into object regions only when a trajectory is finally estimated, using a dynamic programming-based method. Object entry and exit are optimally determined and are not restricted to special spatial zones.

No abstract

In this paper, we present a probabilistic tracking framework that combines sound and vision to achieve more robust and accurate tracking of multiple objects. In a cluttered or noisy scene, our measurements have a non-Gaussian, multimodal distribution. We apply a particle filter to track multiple people using combined audio and video observations. We have applied our algorithm to the domain of tracking people with a stereo-based visual foreground detection algorithm and audio localization using a beamforming technique. Our model also accurately reflects the number of people present. We test the efficacy of our system on a sequence of multiple people moving and speaking in an indoor environment.

No abstract

Steerable microphone arrays provide a flexible infrastructure for audio source separation. In order for them to be used effectively in perceptual user interfaces, there must be a mechanism in place for steering the focus of the array to the sound source. Audio-only steering techniques often perform poorly in the presence of multiple sound sources or strong reverberation. Video-only techniques can achieve high spatial precision but require that the audio and video subsystems be accurately calibrated to preserve this precision. We present an audio-video localization technique that combines the benefits of the two modalities. We implement our technique in a test environment containing multiple stereo cameras and a room-sized microphone array. Our technique achieves an 8.9 dB improvement over a single far-field microphone and a 6.7 dB improvement over source separation based on video-only localization.