Audio-visual Tracking by Density Approximation in a Sequential Bayesian Filtering Framework

Israel Gebru 1 Christine Evers 2 Patrick Naylor 2 Radu Horaud 1
1 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : This paper proposes a novel audiovisual 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 audiovisual approach compared to two benchmark visual trackers.
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Submitted on : Friday, March 17, 2017 - 3:07:06 PM
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Israel Gebru, Christine Evers, Patrick Naylor, Radu Horaud. Audio-visual Tracking by Density Approximation in a Sequential Bayesian Filtering Framework. IEEE Workshop on Hands-free Speech Communication and Microphone Arrays, IEEE Signal Processing Society, Mar 2017, San Francisco, CA, United States. pp.71-75, ⟨10.1109/HSCMA.2017.7895564⟩. ⟨hal-01452167⟩

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