Foreground-Adaptive Background Subtraction

被引:75
作者
McHugh, J. Mike [1 ]
Konrad, Janusz [1 ]
Saligrama, Venkatesh [1 ]
Jodoin, Pierre-Marc [2 ]
机构
[1] Boston Univ, Dept Elect & Comp Engn, Boston, MA 02215 USA
[2] Univ Sherbrooke, Dept Informat, Sherbrooke, PQ J1K 2R1, Canada
基金
美国国家科学基金会;
关键词
Adaptive estimation; background subtraction; hypothesis testing; Markov random fields; motion detection;
D O I
10.1109/LSP.2009.2016447
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Background subtraction is a powerful mechanism for detecting change in a sequence of images that finds many applications. The most successful background subtraction methods apply probabilistic models to background intensities evolving in time; nonparametric and mixture-of-Gaussians models are but two examples. The main difficulty in designing a robust background subtraction algorithm is the selection of a detection threshold. In this paper, we adapt this threshold to varying video statistics by means of two statistical models. In addition to a nonparametric background model, we introduce a foreground model based on small spatial neighborhood to improve discrimination sensitivity. We also apply a Markov model to change labels to improve spatial coherence of the detections. The proposed methodology is applicable to other background models as well.
引用
收藏
页码:390 / 393
页数:4
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