标题：Background Modeling Using Temporal-Local Sample Density Outlier Detection
作者：Zeng, Wei; Yang, Mingqiang; Wang, Feng; Cui, Zhenxing
作者机构：[Zeng, Wei; Yang, Mingqiang; Cui, Zhenxing] Shandong Univ, Sch Informat Sci & Engn, Jinan, Shandong, Peoples R China.; [Wang, Feng] Shandong Inst Pr 更多
会议名称：1st International Conference on Computer Vision and Image Processing (CVIP)
会议日期：FEB 26-28, 2016
来源：PROCEEDINGS OF INTERNATIONAL CONFERENCE ON COMPUTER VISION AND IMAGE PROCESSING, CVIP 2016, VOL 1
关键词：Background modeling; Temporal-local sample density; Outlier detection
摘要：Although researchers have proposed different kinds of techniques for background subtraction, we still need to produce more efficient algorithms in terms of adaptability to multimodal environments. We present a new background modeling algorithm based on temporal-local sample density outlier detection. We use the temporal-local densities of pixel samples as the decision measurement for background classification, with which we can deal with the dynamic backgrounds more efficiently and accurately. Experiment results have shown the outstanding performance of our proposed algorithm with multimodal environments.