A Kalman filtering based data fusion for object tracking

Chin Wen Wu, Yi Nung Chung, Pau Choo Chung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010
Pages2291-2295
Number of pages5
DOIs
Publication statusPublished - 2010 Sep 1
Event5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010 - Taichung, Taiwan
Duration: 2010 Jun 152010 Jun 17

Other

Other5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010
CountryTaiwan
CityTaichung
Period10-06-1510-06-17

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All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

Wu, C. W., Chung, Y. N., & Chung, P. C. (2010). A Kalman filtering based data fusion for object tracking. In Proceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010 (pp. 2291-2295). [5516708] https://doi.org/10.1109/ICIEA.2010.5516708