Audiovisual Tracking Using STAC Sensors
In this paper we present an audiovisual tracking algorithm using a stereo audio and cycloptic vision (STAC) sensor. A STAC sensor is composed of a single camera mounted between two microphones. Target localization is performed using color-based change detection in the video modality and on estimation of time difference of arrival (TDOA) between the two microphones in the audio modality. The TDOA is computed by means of a multi-band generalized cross correlation (GCC) analysis. The estimated directions of arrival are then ltered using a Riccati Kalman Iter . The visual and audio estimates are nally integrated, at likelihood level, into a particle Iter that uses a zero-order motion model. We demonstrate that the Kalman ltering improves the accuracy of the audio source localization and sensor fusion with particle ltering under image occlusions. We also demonstrate the proposed audiovisual tracker to generate metadata for very low bitrate teleconferencing.