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https://hdl.handle.net/2440/29559
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Type: | Conference paper |
Title: | Augmented particle filtering for efficient visual tracking |
Author: | Shen, C. Brooks, M. Van Den Hengel, A. |
Citation: | Proceedings of the IEEE International Conference on Image Processing, volume 3, 11-14 September, 2005:pp.856-9 |
Publisher: | IEEE |
Publisher Place: | USA |
Issue Date: | 2005 |
Series/Report no.: | IEEE International Conference on Image Processing ICIP |
ISBN: | 0-7803-9134-9 |
ISSN: | 1522-4880 |
Conference Name: | IEEE International Conference on Image Processing (2005 : Genoa, Italy) |
Editor: | Regazzoni, C. De Natale, F. |
Statement of Responsibility: | Chunhua Shen Brooks, M.J. van den Hengel, A. |
Abstract: | Visual tracking is one of the key tasks in computer vision. The particle filter algorithm has been extensively used to tackle this problem due to its flexibility. However the conventional particle filter uses system transition as the proposal distribution, frequently resulting in poor priors for the filtering step. The main reason is that it is difficult, if not impossible, to accurately model the target's motion. Such a proposal distribution does not take into account the current observations. It is not a trivial task to devise a satisfactory proposal distribution for the particle filter. In this paper we advance a general augmented particle filtering framework for designing the optimal proposal distribution. The essential idea is to augment a second filter's estimate into the proposal distribution design. We then show that several existing improved particle filters can be rationalised within this general framework. Based on this framework we further propose variant algorithms for robust and efficient visual tracking. Experiments indicate that the augmented particle filters are more efficient and robust than the conventional particle filter. |
Description: | Copyright © 2005 IEEE |
DOI: | 10.1109/ICIP.2005.1530527 |
Published version: | http://dx.doi.org/10.1109/icip.2005.1530527 |
Appears in Collections: | Aurora harvest 2 Computer Science publications |
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hdl_29559.pdf | 242.06 kB | Author's post-print | View/Open |
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