%0 Conference Paper
%B Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on
%D 2007
%T Multimodal Tracking for Smart Videoconferencing and Video Surveillance
%A Zotkin,Dmitry N
%A Raykar,V.C.
%A Duraiswami, Ramani
%A Davis, Larry S.
%K (numerical
%K 3D
%K algorithm;smart
%K analysis;least
%K approximations;particle
%K arrays;nonlinear
%K cameras;multiple
%K Carlo
%K estimator;multimodal
%K filter;self-calibration
%K Filtering
%K least
%K likelihood
%K methods);teleconferencing;video
%K methods;image
%K microphone
%K MOTION
%K motion;Monte-Carlo
%K problem;particle
%K processing;video
%K signal
%K simulations;maximum
%K squares
%K surveillance;
%K surveillance;Monte
%K tracking;multiple
%K videoconferencing;video
%X Many applications require the ability to track the 3-D motion of the subjects. We build a particle filter based framework for multimodal tracking using multiple cameras and multiple microphone arrays. In order to calibrate the resulting system, we propose a method to determine the locations of all microphones using at least five loudspeakers and under assumption that for each loudspeaker there exists a microphone very close to it. We derive the maximum likelihood (ML) estimator, which reduces to the solution of the non-linear least squares problem. We verify the correctness and robustness of the multimodal tracker and of the self-calibration algorithm both with Monte-Carlo simulations and on real data from three experimental setups.
%B Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on
%P 1 - 2
%8 2007/06//
%G eng
%R 10.1109/CVPR.2007.383525