热门
Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurements
文献类型：期刊论文
| 作者 | Cao WF(曹文飞); Wang Y(王尧); Sun J(孙剑); Meng DY(孟德宇); Yang, Can; Cichocki, Andrzej; Xu ZB(徐宗本) |
| 刊名 | IEEE Transactions on Image Processing
 |
| 出版日期 | 2016 |
| 卷号 | 25期号:9页码:4075-4090 |
| ISSN号 | 1057-7149 |
| 产权排序 | 2 |
| 通讯作者 | 王尧 |
| 中文摘要 | Background subtraction has been a fundamental and widely studied task in video analysis, with a wide range of applications in video surveillance, teleconferencing, and 3D modeling. Recently, motivated by compressive imaging, background subtraction from compressive measurements (BSCM) is becoming an active research task in video surveillance. In this paper, we propose a novel tensor-based robust principal component analysis (TenRPCA) approach for BSCM by decomposing video frames into backgrounds with spatial-temporal correlations and foregrounds with spatio-temporal continuity in a tensor framework. In this approach, we use 3D total variation to enhance the spatio-temporal continuity of foregrounds, and Tucker decomposition to model the spatio-temporal correlations of video background. Based on this idea, we design a basic tensor RPCA model over the video frames, dubbed as the holistic TenRPCA model. To characterize the correlations among the groups of similar 3D patches of video background, we further design a patch-group-based tensor RPCA model by joint tensor Tucker decompositions of 3D patch groups for modeling the video background. Efficient algorithms using the alternating direction method of multipliers are developed to solve the proposed models. Extensive experiments on simulated and real-world videos demonstrate the superiority of the proposed approaches over the existing state-of-the-art approaches. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| 研究领域[WOS] | Computer Science ; Engineering |
| 关键词[WOS] | OBJECT DETECTION ; VIDEO ; TRACKING ; SPARSE ; ALGORITHM |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000394573100001 |
| 源URL | [http://ir.sia.cn/handle/173321/18782]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 推荐引用方式 GB/T 7714 | Cao WF,Wang Y,Sun J,et al. Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurements[J]. IEEE Transactions on Image Processing,2016,25(9):4075-4090. |
| APA | Cao WF.,Wang Y.,Sun J.,Meng DY.,Yang, Can.,...&Xu ZB.(2016).Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurements.IEEE Transactions on Image Processing,25(9),4075-4090. |
| MLA | Cao WF,et al."Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurements".IEEE Transactions on Image Processing 25.9(2016):4075-4090. |
入库方式： OAI收割
来源：沈阳自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。