FOREGROUND DETECTION USING LOW RANK AND STRUCTURED SPARSITY

被引：0
|
作者：
Yao, Jiawen [1 ]
Liu, Xin [1 ]
Qi, Chun [1 ]
机构：
[1] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian 710049, Peoples R China
来源：
2014 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME) | 2014年
关键词：
Foreground detection; Structured sparsity; Low-rank modeling; Background subtraction; COMPLEX BACKGROUNDS; MIXTURE;
D O I：
暂无
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
In this paper, a novel foreground detection method based on two-stage framework is presented. In the first stage, a class of structured sparsity-inducing norms is introduced to model moving objects in videos and thus regard the observed sequence as being made up of the sum of a low-rank matrix and a structured sparse outlier matrix. In virtue of adaptive parameters, the proposed method includes a motion saliency measurement to dynamically estimate the support of the foreground in the second stage. Experiments on challenging datasets demonstrate that the proposed approach outperforms the state-of-the-art methods and works effectively on a wide range of complex videos.
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页数：6
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