Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICIF.2007.4408087
Title: Multiple sensor multiple object tracking with GMPHD filter
Authors: Pham, N.T.
Huang, W.
Ong, S.H. 
Keywords: Bearing and range tracking
Gaussian mixture probability hypothesis density
Random finite set
Speaker tracking
Issue Date: 2007
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
URI: http://scholarbank.nus.edu.sg/handle/10635/71056
ISBN: 0662478304
DOI: 10.1109/ICIF.2007.4408087
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

16
checked on Dec 13, 2017

Page view(s)

15
checked on Dec 16, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.