Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/55344
Citations | ||
Scopus | Web of ScienceĀ® | Altmetric |
---|---|---|
?
|
?
|
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wang, H. | - |
dc.contributor.author | Suter, D. | - |
dc.contributor.editor | Tang, Y.Y. | - |
dc.contributor.editor | Wang, S.P. | - |
dc.contributor.editor | Lorette, G. | - |
dc.contributor.editor | Yeung, D.S. | - |
dc.contributor.editor | Yan, H. | - |
dc.date.issued | 2006 | - |
dc.identifier.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 | - |
dc.identifier.isbn | 0769525210 | - |
dc.identifier.issn | 1051-4651 | - |
dc.identifier.uri | http://hdl.handle.net/2440/55344 | - |
dc.description.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. | - |
dc.description.statementofresponsibility | Hanzi Wang and David Suter | - |
dc.language.iso | en | - |
dc.publisher | IEEE | - |
dc.relation.ispartofseries | International Conference on Pattern Recognition | - |
dc.source.uri | http://dx.doi.org/10.1109/icpr.2006.312 | - |
dc.title | Background Subtraction Based on a Robust Consensus Method | - |
dc.type | Conference paper | - |
dc.contributor.conference | International Conference on Pattern Recognition (18th : 2006 : Hong Kong) | - |
dc.identifier.doi | 10.1109/ICPR.2006.312 | - |
dc.publisher.place | Online | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/DP0452416 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/DP0452416 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Suter, D. [0000-0001-6306-3023] | - |
Appears in Collections: | Aurora harvest Computer Science publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.