.png){kind=icon}
返回
Dynamic background estimation and complementary learning for pixel-wise foreground/background segmentation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2016.01.031.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Change and motion detection plays a basic and guiding role in surveillance video analysis. Since most outdoor surveillance videos are taken in native and complex environments, these static backgrounds change in some unknown patterns, which make perfect foreground extraction very difficult. This paper presents two universal modifications for pixel-wise foreground/background segmentation: dynamic background estimation and complementary learning. These two modifications are embedded in three classic background subtraction algorithms: probability based background subtraction (Gaussian mixture model, GMM), sample based background subtraction (visual background extractor, ViBe) and code words based background subtraction (code book, CB). Experiments on several popular public datasets prove the effectiveness and real-time performance of the proposed method. Both GMM and CB with the proposed modifications have better performance than the original versions. Especially, ViBe with the modifications outperforms some state-of-art algorithms presented on the CHANGEDETECTION website. (C) 2016 Elsevier Ltd. All rights reserved.
Keyword:
Background subtraction (BS)
GMM
ViBe
CB
Dynamic background estimation
Complementary learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
.png){kind=icon}
分享收藏
查看更多内容