A Multi-view Learning Approach to Foreground Detection for Traffic Surveillance Applications
文献类型：期刊论文
| 作者 | Wang, Kunfeng1 ; Liu, Yuqiang1,2; Gou, Chao1,3; Wang, Fei-Yue1,4; Wang, Kunfeng(王坤峰)
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| 刊名 | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
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| 出版日期 | 2016-06-01 |
| 卷号 | 65期号:6页码:4144-4158 |
| 关键词 | Conditional independence foreground detection heterogeneous features Markov random field (MRF) multi-view learning |
| 通讯作者 | Wang, Kunfeng(王坤峰) |
| 英文摘要 | This paper proposes an effective multi-view learning approach to foreground detection for traffic surveillance applications. This approach involves three main steps. First, a reference background image is generated via temporal median filtering, and multiple heterogeneous features (including brightness variation, chromaticity variation, and texture variation, each of which represents a unique view) are extracted from the video sequence. Then, a multi-view learning strategy is devised to online estimate the conditional probability densities for both the foreground and the background. The probability densities of three features are approximately conditionally independent and are estimated with kernel density estimation. Pixel soft labeling is conducted by using Bayes rule, and the pixelwise foreground posteriors are computed. Finally, a Markov random field is constructed to incorporate the spatiotemporal context into the foreground/background decision model. The belief propagation algorithm is used to label each pixel of the current frame. Experimental results verify that the proposed approach is effective to detect foreground objects from challenging traffic environments and outperforms some state-of-the-art methods. |
| WOS标题词 | Science & Technology ; Technology |
| 学科主题 | CIVIL ENGINEERING |
| 类目[WOS] | Engineering, Electrical & Electronic ; Telecommunications ; Transportation Science & Technology |
| 研究领域[WOS] | Engineering ; Telecommunications ; Transportation |
| 关键词[WOS] | GAUSSIAN MIXTURE MODEL ; REAL-TIME TRACKING ; BACKGROUND SUBTRACTION ; OBJECT DETECTION ; VISUAL SURVEILLANCE ; CAST SHADOWS ; SEGMENTATION ; SUPPRESSION ; EDGE |
| 收录类别 | SCI |
| 原文出处 | http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7359142 |
| 语种 | 英语 |
| WOS记录号 | WOS:000380068500026 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/10859]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
| 通讯作者 | Wang, Kunfeng(王坤峰) |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.China Acad Railway Sci, Beijing 100081, Peoples R China 3.Qingdao Acad Intelligent Ind, Qingdao 266109, Peoples R China 4.Natl Univ Def Technol, Res Ctr Computat Expt & Parallel Syst, Changsha 410073, Hunan, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Kunfeng,Liu, Yuqiang,Gou, Chao,et al. A Multi-view Learning Approach to Foreground Detection for Traffic Surveillance Applications[J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,2016,65(6):4144-4158. |
| APA | Wang, Kunfeng,Liu, Yuqiang,Gou, Chao,Wang, Fei-Yue,&Wang, Kunfeng.(2016).A Multi-view Learning Approach to Foreground Detection for Traffic Surveillance Applications.IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,65(6),4144-4158. |
| MLA | Wang, Kunfeng,et al."A Multi-view Learning Approach to Foreground Detection for Traffic Surveillance Applications".IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 65.6(2016):4144-4158. |
入库方式： OAI收割
来源：自动化研究所
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