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Please use this identifier to cite or link to this item:
http://ir.ncue.edu.tw/ir/handle/987654321/11831
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| Title: | A Kalman Filtering Based Data Fusion for Object Tracking |
| Authors: | Wu, Chin-W;Chung, Yi-Nung;Chung, Pau-Choo |
| Contributors: | 電機工程學系 |
| Keywords: | Object tracking;Multiple cameras;Data fusion;Kalman filter |
| Date: | 2010-06
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| Issue Date: | 2012-07-02T02:11:23Z
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| Publisher: | IEEE |
| Abstract: | To solve that single camera has its limitation of field of view, this paper proposed an object tracking method using multiple camera data fusion in image sequences. In this approach, a tracking filter and a multiple-view data fusion algorithm are applied. An estimation structure, called
hierarchical estimation, is used to generate local and global estimate and to combine the estimates obtained from each camera views to form a global estimate. The advantage of this approach is the data of one camera view complements that of another camera view in order to obtain better target measurement information and to make more accurate estimates. A set of image sequences from multiple views are applied to
evaluate performance. Computer simulation and experimental results indicate that this approach successfully tracks objects and
has good estimation. |
| Relation: | 2010 the 5th IEEE Conference on,Industrial Electronics and Applications (ICIEA), June 15-17, 2010: 2291-2295 |
| Appears in Collections: | [電機工程學系] 會議論文
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