SPEAKER TRACKING USING PARTICLE FILTER SENSOR FUSION
Yunqiang Chen, Yong Rui · 2004
in Proc. of Asian Conference on Computer Vision (ACCV), 2004 Sensor fusion for object tracking has become an active research direction during the past few years. But how to do it in a robust and principled way is still an open problem. In this paper, we propose a new fusion framework that combines both the bottom-up and top-down approaches to probabilistically fuse multiple sensing modalities. At the lower level, individual vision and audio trackers are designed to generate effective proposals for the fuser. At the higher level, the fuser performs reliable tracking by verifying hypotheses over multiple likelihood models from different sensors. Unlike the traditional fusion algorithms, the proposed framework is a closed-loop system where the fuser and trackers coordinate their tracking information. Furthermore, the proposed framework provides a natural scheme to evaluate the performance of the individual trackers and dynamically updates their object states in nonstationary environments and hence much more robust in longterm tracking. We present a real-time speaker tracking system by fusing object contour, color and sound source localization. Robust tracking results are achieved.