Diversity Sampling Based Kernel Density Estimation for Background Modeling  

作　　者：毛燕芬 施鹏飞 

机构地区：[1]Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai 200030, P.R. China

出　　处：《Journal of Shanghai University(English Edition)》2005年第6期506-509,共4页上海大学学报（英文版）

基　　金：Project supported by National Basic Research Program of Chinaon Urban Traffic Monitoring and Management System(Grant No .TG1998030408)

摘　　要：A novel diversity-sampling based nonparametric multi-modal background model is proposed. Using the samples having more popular and various intensity values in the training sequence, a nonparametric model is built for background subtraction. According to the related intensifies, different weights are given to the distinct samples in kernel density estimation. This avoids repeated computation using all samples, and makes computation more efficient in the evaluation phase. Experimental results show the validity of the diversity- sampling scheme and robustness of the proposed model in moving objects segmentation. The proposed algorithm can be used in outdoor surveillance systems.A novel diversity-sampling based nonparametric multi-modal background model is proposed. Using the samples having more popular and various intensity values in the training sequence, a nonparametric model is built for background subtraction. According to the related intensifies, different weights are given to the distinct samples in kernel density estimation. This avoids repeated computation using all samples, and makes computation more efficient in the evaluation phase. Experimental results show the validity of the diversity- sampling scheme and robustness of the proposed model in moving objects segmentation. The proposed algorithm can be used in outdoor surveillance systems.

关 键 词：background subtraction diversity sampling kernel density estimation multi-modal background model 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]