English  |  正體中文  |  简体中文  |  Items with full text/Total items : 6491/11663
Visitors : 24746895      Online Users : 50
RC Version 3.2 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Adv. Search

Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/11831

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
Issue Date: 2012-07-02T02:11:23Z
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

Files in This Item:

File SizeFormat

All items in NCUEIR are protected by copyright, with all rights reserved.


DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback