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Efficient background modeling through incremental Support Vector Data Description
2008
Pattern Recognition (ICPR), Proceedings of the International Conference on
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modeling is an essential and important part of many high-level video processing applications. Recently, the Support Vector Data Description (SVDD) has been introduced for novelty detection when only one class of data is available, i.e. background pixels. This paper proposes a method to efficiently train an SVDD and compares the performance of this training algorithm with the traditional SVDD training techniques. We compare the performance of our method with traditional SVDD and other
doi:10.1109/icpr.2008.4761328
dblp:conf/icpr/TavakkoliNBN08
fatcat:tg34b3wmpjfklbmrxvan273sqy