Audio-visual tracking by density approximation in a sequential Bayesian filtering framework

Gebru, Israel D., Evers, Christine, Naylor, Patrick A. and Horaud, Radu (2017) Audio-visual tracking by density approximation in a sequential Bayesian filtering framework. In 2017 Hands-Free Speech Communications and Microphone Arrays, HSCMA 2017 - Proceedings. IEEE. pp. 71-75 . (doi:10.1109/HSCMA.2017.7895564).

Abstract

This paper proposes a novel audio-visual tracking approach that exploits constructively audio and visual modalities in order to estimate trajectories of multiple people in a joint state space. The tracking problem is modeled using a sequential Bayesian filtering framework. Within this framework, we propose to represent the posterior density with a Gaussian Mixture Model (GMM). To ensure that a GMM representation can be retained sequentially over time, the predictive density is approximated by a GMM using the Unscented Transform. While a density interpolation technique is introduced to obtain a continuous representation of the observation likelihood, which is also a GMM. Furthermore, to prevent the number of mixtures from growing exponentially over time, a density approximation based on the Expectation Maximization (EM) algorithm is applied, resulting in a compact GMM representation of the posterior density. Recordings using a camcorder and microphone array are used to evaluate the proposed approach, demonstrating significant improvements in tracking performance of the proposed audio-visual approach compared to two benchmark visual trackers.

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Contributors

Author: Christine Evers

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