Multiple sensor multiple object tracking with GMPHD filter
Tracking objects using multiple sensors is more efficient than those using one sensor. In this paper, we proposed a method to fuse data from multiple sensors in Gaussian mixture probability hypothesis density filter. This method can avoid the data association problem in multi-sensor multi-object tra...
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Published in | 2007 10th International Conference on Information Fusion pp. 1 - 7 |
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Main Authors | , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.07.2007
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Subjects | |
Online Access | Get full text |
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Summary: | Tracking objects using multiple sensors is more efficient than those using one sensor. In this paper, we proposed a method to fuse data from multiple sensors in Gaussian mixture probability hypothesis density filter. This method can avoid the data association problem in multi-sensor multi-object tracking. Moreover, it is more reliable and less computational than particle probability hypothesis density filter for multi-sensor multi-object tracking. We demonstrated the efficient of the approach by applications such as bearing and range tracking, and multiple speaker tracking. |
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DOI: | 10.1109/ICIF.2007.4408087 |