Dieter Schulz

Robust joint audio-video localization in video conferencing using reliability information

This study builds on our previous IMTC03 paper (D. Lo. R. Goubran et al, Proc. 20th IEEE Instrument. and Meas., vol.2. p.1414-1418, 2003). Both this study and our previous paper use data fusion to combine results from multiple audio and video localizers. The two studies differ in the type of data fusion engine used. The former study explored the use of a summing voter, whereas this current study employs the use of a Bayesian network. The novelty of both papers is the use of reliability estimates to improve the overall localization performance and robustness. Reliability estimates, that are derived based on known physical properties of each individual localizer, were introduced into the fusion engines to achieve better performance. Although the summing voter fusion engine used in the last paper improves the overall localization performance, it does not take into account the unique characteristics of each localizer. The Bayesian network allows these characteristics to be included as part of the fusion process. In this study, we investigate the impact of (1) using a Bayesian network as the data fusion engine, and (2) adding reliability estimates into the fusion engine.This paper proposes a new method for performing joint audio-video localization that explores the reliability of the individual localization estimates. The reliability information is estimated from the audio and video data separately. The proposed method uses this reliability information in conjunction with a simple summing voter to dynamically discriminate erroneous outputs from the audio and video localizers while performing fusion on the localization results. Based on the voter output, a majority rule is then used to make the final decision of the active talkers current location. The results show that adding the reliability information during fusion improves localization performance when compared with audio only, video only and joint audio-video using straight summing fusion localization methods. The computational complexity of the proposed method is comparable to the existing ones.