Gestalt principle based change detection and background reconstruction
Qiu, Shi1; Dong, Yongsheng1; Lu, Xiaoqiang1; Du, Ming2,3
2016
会议名称4th chinese conference on intelligent visual surveillance, ivs 2016
会议日期2016-10-19
会议地点beijing, china
卷号664 ccis
页码20-29
英文摘要

gaussian mixture model based detection algorithms can easily lead to fragmentary due to the fixed number of gaussian components. in this paper, we propose a gestalt principle based change target extraction method, and further present a background reconstruction algorithm. in particular, firstly we applied the gaussian mixture model to extract the moving target as others did but this may lead to incomplete extraction. secondly, we have also tried to apply the frame difference method to extract the moving target more precisely. finally, we determine to build a static background according to relationships between each frame of a moving target. experiment results reveal that the proposed detection method outperforms the other three representative detection methods. moreover, our background reconstruction algorithm is also proved to be very effective and robust in reconstructing the backgrounds of a video. © springer nature singapore pte ltd. 2016.

收录类别EI ; ISTP
产权排序1
会议录intelligent visual surveillance - 4th chinese conference, ivs 2016, proceedings
会议录出版者springer verlag
语种英语
ISSN号18650929
ISBN号9789811034756
内容类型会议论文
源URL[http://ir.opt.ac.cn/handle/181661/28715]  
专题西安光学精密机械研究所_光学影像学习与分析中心
作者单位1.Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, China
2.College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
3.Fujian Key Lab of Medical Instrument and Pharmaceutical Technology, Fuzhou, China
推荐引用方式
GB/T 7714
Qiu, Shi,Dong, Yongsheng,Lu, Xiaoqiang,et al. Gestalt principle based change detection and background reconstruction[C]. 见:4th chinese conference on intelligent visual surveillance, ivs 2016. beijing, china. 2016-10-19.
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