Moving target detection based on KDE combining local texture feature

被引：0
|
作者：
Jin J. [1 ]
Dang J.-W. [1 ]
Zhai F.-W. [1 ]
Wang Y.-P. [2 ]
Shen D. [1 ]
Zhang Z.-H. [2 ]
机构：
[1] School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou
[2] Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphics & Image Processing, Gansu Computing Center, Lanzhou
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2019年 / 49卷 / 02期
关键词：
Information processing technology; Kernel density estimation; Local texture feature; Moving targets detection; Neighborhood correlation;
D O I：
10.13229/j.cnki.jdxbgxb20171235
中图分类号：
学科分类号：
摘要：
To solve the problems of illumination change and moving shadow in video moving target detection, a new Kernel Density Estimation (KDE) method is proposed based on multi-dimension characteristics. An improved local texture feature binary pattern is put forward which is robust to noise and variant gray scale. In background modeling, the local texture feature and color feature are fused for KDE, and the neighborhood correlation is integrated to suppress false foreground. Experimental results show that the proposed method has good robustness to slow illumination change and soft project shadows and enhances F_measure by 18% comparing to the same system algorithms based on local texture feature or KDE model. Comparing to existing state-of-the-art methods, the proposed method can enhance processing speed by 50% while maintaining the same detection result. The method has comprehensive performance, which balances detection effect and time cost. © 2019, Jilin University Press. All right reserved.
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收藏
页码：647 / 655
页数：8
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