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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Nam Trung Pham Weimin Huang Ong, S.H. |
| Copyright Year | 2007 |
| Description | Author affiliation: Inst. for Infocomm Res., Singapore (Nam Trung Pham; Weimin Huang) |
| 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. |
| Starting Page | 1 |
| Ending Page | 7 |
| File Size | 752833 |
| Page Count | 7 |
| File Format | |
| ISBN | 9780662458043 |
| DOI | 10.1109/ICIF.2007.4408087 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-07-09 |
| Publisher Place | Canada |
| Access Restriction | Subscribed |
| Rights Holder | ISIF |
| Subject Keyword | Sensor fusion State estimation Fuses Particle tracking Monte Carlo methods Sensor phenomena and characterization Filters Sliding mode control Convergence Data mining speaker tracking Random finite set Gaussian mixture probability hypothesis density bearing and range tracking |
| Content Type | Text |
| Resource Type | Article |
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