CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
A unified model sharing framework for moving object detection
Chen, Yingying1; Wang, Jinqiao1; Xu, Min2; He, Xiangjian2; Lu, Hanqing1
Source PublicationSIGNAL PROCESSING
2016-07-01
Issue124Pages:72-80
SubtypeArticle
AbstractMillions of surveillance cameras have been installed in public areas, producing vast amounts of video data every day. It is an urgent need to develop intelligent techniques to automatically detect and segment moving objects which have wide applications. Various approaches have been developed for moving object detection based on background modeling in the literature. Most of them focus on temporal information but partly or totally ignore spatial information, bringing about sensitivity to noise and background motion. In this paper, we propose a unified model sharing framework for moving object detection. To begin with, to exploit the spatial-temporal correlation across different pixels, we establish a many-to-one correspondence by model sharing between pixels, and a pixel is labeled into foreground or background by searching an optimal matched model in the neighborhood. Then a random sampling strategy is introduced for online update of the shared models. In this way, we can reduce the total number of models dramatically and match a proper model for each pixel accurately. Furthermore, existing approaches can be naturally embedded into the proposed sharing framework. Two popular approaches, statistical model and sample consensus model, are used to verify the effectiveness. Experiments and comparisons on ChangeDetection benchmark 2014 demonstrate the superiority of the model sharing solution. (C) 2015 Elsevier B.V. All rights reserved.
KeywordMoving Object Detection Background Subtraction Shared Model
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.sigpro.2015.10.011
WOS KeywordMULTILABEL IMAGE CLASSIFICATION
Indexed BySCI
Language英语
Funding Organization863 Program(2014AA015104) ; National Natural Science Foundation of China(61273034 ; 61332016)
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000373538100009
Citation statistics
Cited Times:7[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11074
Collection模式识别国家重点实验室_图像与视频分析
Corresponding AuthorWang, Jinqiao
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
2.Global Big Data Technologies Centre,University of Technology
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Chen, Yingying,Wang, Jinqiao,Xu, Min,et al. A unified model sharing framework for moving object detection[J]. SIGNAL PROCESSING,2016(124):72-80.
APA Chen, Yingying,Wang, Jinqiao,Xu, Min,He, Xiangjian,&Lu, Hanqing.(2016).A unified model sharing framework for moving object detection.SIGNAL PROCESSING(124),72-80.
MLA Chen, Yingying,et al."A unified model sharing framework for moving object detection".SIGNAL PROCESSING .124(2016):72-80.
Files in This Item: Download All
File Name/Size DocType Version Access License
A unified model shar(1865KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Chen, Yingying]'s Articles
[Wang, Jinqiao]'s Articles
[Xu, Min]'s Articles
Baidu academic
Similar articles in Baidu academic
[Chen, Yingying]'s Articles
[Wang, Jinqiao]'s Articles
[Xu, Min]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Chen, Yingying]'s Articles
[Wang, Jinqiao]'s Articles
[Xu, Min]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: A unified model sharing framework for moving.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.