中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Extended scale invariant local binary pattern for background subtraction

文献类型:期刊论文

作者Zeng, D. D.; Zhu, M.; Xu, F.; Zhou, T. X.
刊名Iet Image Processing
出版日期2018
卷号12期号:8页码:1292-1302
关键词visual surveillance density-estimation segmentation objects scenes model Computer Science Engineering Imaging Science & Photographic Technology
ISSN号1751-9659
DOI10.1049/iet-ipr.2016.1026
英文摘要Background subtraction based on change detection is the first step in many video surveillance systems, an effective background subtraction algorithm should distinguish foreground from the background sensitively, and adapt to the variation of background scenes robustly. In this study, the authors propose a robust background subtraction algorithm which takes advantages of local texture features represented by an extended scale invariant local binary pattern and colour intensities to characterise pixel representations. Local texture features achieve good tolerance against illumination variations in rich texture regions but not so efficiently on uniform regions, so a photometric invariant colour measurement is proposed to overcome its limitation. Both quantitative and qualitative evaluations carried out on a well- known change detection dataset are provided to demonstrate the effectiveness of the proposed algorithm.
源URL[http://ir.ciomp.ac.cn/handle/181722/60989]  
专题中国科学院长春光学精密机械与物理研究所
推荐引用方式
GB/T 7714
Zeng, D. D.,Zhu, M.,Xu, F.,et al. Extended scale invariant local binary pattern for background subtraction[J]. Iet Image Processing,2018,12(8):1292-1302.
APA Zeng, D. D.,Zhu, M.,Xu, F.,&Zhou, T. X..(2018).Extended scale invariant local binary pattern for background subtraction.Iet Image Processing,12(8),1292-1302.
MLA Zeng, D. D.,et al."Extended scale invariant local binary pattern for background subtraction".Iet Image Processing 12.8(2018):1292-1302.

入库方式: OAI收割

来源:长春光学精密机械与物理研究所

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