Abstract
This paper presents an object localization and tracking algorithm integrating audio and video-based object localization results. A face tracking algorithm and a microphone array are used to compute two single-modality speaker position estimates. These position estimates are then combined into a global position estimate using a decentralized Kalman filter. Experiments with a model railway show that such an approach yields more robust results for audio-visual object tracking than either modality by itself.
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