| Pixel-to-Model Distance for Robust Background Reconstruction | |
Yang, Lu1; Cheng, Hong1; Su, Jianan1; Li, Xuelong2
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| 作者部门 | 光学影像学习与分析中心 |
| 2016-05-01 | |
| 发表期刊 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
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| ISSN | 1051-8215 |
| 卷号 | 26期号:5页码:903-916 |
| 产权排序 | 2 |
| 摘要 | Background information is crucial for many video surveillance applications such as object detection and scene understanding. In this paper, we present a novel pixel-to-model (P2M) paradigm for background modeling and restoration in surveillance scenes. In particular, the proposed approach models the background with a set of context features for each pixel, which are compressively sensed from local patches. We determine whether a pixel belongs to the background according to the minimum P2M distance, which measures the similarity between the pixel and its background model in the space of compressive local descriptors. The pixel feature descriptors of the background model are properly updated with respect to the minimum P2M distance. Meanwhile, the neighboring background model will be renewed according to the maximum P2M distance to handle ghost holes. The P2M distance plays an important role of background reliability in the 3-D spatial-temporal domain of surveillance videos, leading to the robust background model and recovered background videos. We applied the proposed P2M distance for foreground detection and background restoration on synthetic and real-world surveillance videos. Experimental results show that the proposed P2M approach outperforms the state-of-the-art approaches both in indoor and outdoor surveillance scenes. |
| 文章类型 | Article |
| 关键词 | Background Modeling Background Restoration Local Context Descriptor Pixel-to-model (P2m) Distance Video Surveillance |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1109/TCSVT.2015.2424052 |
| 收录类别 | SCI ; EI |
| 关键词[WOS] | FOREGROUND OBJECT DETECTION ; GAUSSIAN MIXTURE MODEL ; CODEBOOK MODEL ; ENERGY MINIMIZATION ; VIDEO SURVEILLANCE ; MOTION DETECTION ; SUBTRACTION ; SEGMENTATION ; EFFICIENT ; PREDICTION |
| 语种 | 英语 |
| WOS研究方向 | Engineering |
| 项目资助者 | National Natural Science Foundation of China(61305033 ; Key Research Program through the Chinese Academy of Sciences(KGZD-EW-T03) ; Fundamental Research Funds for the Central Universities(ZYGX2013J088) ; Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry ; 61273256 ; 61125106) |
| WOS类目 | Engineering, Electrical & Electronic |
| WOS记录号 | WOS:000375707500009 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/28132 |
| 专题 | 光谱成像技术研究室 |
| 作者单位 | 1.Univ Elect Sci & Technol China, Ctr Robot, Chengdu 611731, Peoples R China 2.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr OPT IMagery Anal & Learning, State Key Lab Transient Opt & Photon, Xian 710119, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Lu,Cheng, Hong,Su, Jianan,et al. Pixel-to-Model Distance for Robust Background Reconstruction[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2016,26(5):903-916. |
| APA | Yang, Lu,Cheng, Hong,Su, Jianan,&Li, Xuelong.(2016).Pixel-to-Model Distance for Robust Background Reconstruction.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,26(5),903-916. |
| MLA | Yang, Lu,et al."Pixel-to-Model Distance for Robust Background Reconstruction".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 26.5(2016):903-916. |
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| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Pixel-to-Model Dista（6615KB） | 期刊论文 | 作者接受稿 | 限制开放 | CC BY-NC-SA | 请求全文 | |
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