Generalized Stauffer-Grimson background subtraction for dynamic scenes

被引:48
作者
Chan, Antoni B. [1 ]
Mahadevan, Vijay [1 ]
Vasconcelos, Nuno [1 ]
机构
[1] Univ Calif San Diego, Dept Elect & Comp Engn, La Jolla, CA 92093 USA
关键词
Dynamic textures; Background models; Background subtraction; Mixture models; Adaptive models;
D O I
10.1007/s00138-010-0262-3
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We propose an adaptive model for backgrounds containing significant stochastic motion (e.g. water). The new model is based on a generalization of the Stauffer-Grimson background model, where each mixture component is modeled as a dynamic texture. We derive an online K-means algorithm for updating the parameters using a set of sufficient statistics of the model. Finally, we report on experimental results, which show that the proposed background model both quantitatively and qualitatively outperforms state-of-the-art methods in scenes containing significant background motions.
引用
收藏
页码:751 / 766
页数:16
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