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https://hdl.handle.net/2440/55344
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Type: | Conference paper |
Title: | Background Subtraction Based on a Robust Consensus Method |
Author: | Wang, H. Suter, D. |
Citation: | Proceedings. 18th International Conference on Pattern Recognition, 20-24 August, 2006, Hong Kong, Volume 1/ Y. Y. Tang, S. P. Wang, G. Lorette, D. S. Yeung and H. Yan (eds.): pp.223-226 |
Publisher: | IEEE |
Publisher Place: | Online |
Issue Date: | 2006 |
Series/Report no.: | International Conference on Pattern Recognition |
ISBN: | 0769525210 9780769525211 |
ISSN: | 1051-4651 |
Conference Name: | International Conference on Pattern Recognition (18th : 2006 : Hong Kong) |
Editor: | Tang, Y.Y. Wang, S.P. Lorette, G. Yeung, D.S. Yan, H. |
Statement of Responsibility: | Hanzi Wang and David Suter |
Abstract: | Statistical background modeling is a fundamental and important part of many visual tracking systems and of other computer vision applications. In this paper, we presents an effective and adaptive background modeling method for detecting foreground objects in both static and dynamic scenes. The proposed method computes SAmple CONsensus (SACON) of the background samples and estimates a statistical model per pixel. Numerous experiments on both indoor and outdoor video sequences show that the proposed method, compared with several state-of-the-art methods, can achieve very promising performance. |
DOI: | 10.1109/ICPR.2006.312 |
Grant ID: | http://purl.org/au-research/grants/arc/DP0452416 http://purl.org/au-research/grants/arc/DP0452416 |
Published version: | http://dx.doi.org/10.1109/icpr.2006.312 |
Appears in Collections: | Aurora harvest Computer Science publications |
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