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|Title:||Multiple sensor multiple object tracking with GMPHD filter|
|Keywords:||Bearing and range tracking|
Gaussian mixture probability hypothesis density
Random finite set
|Source:||Pham, N.T.,Huang, W.,Ong, S.H. (2007). Multiple sensor multiple object tracking with GMPHD filter. FUSION 2007 - 2007 10th International Conference on Information Fusion : -. ScholarBank@NUS Repository. https://doi.org/10.1109/ICIF.2007.4408087|
|Abstract:||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.|
|Source Title:||FUSION 2007 - 2007 10th International Conference on Information Fusion|
|Appears in Collections:||Staff Publications|
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