| Incrementally Detecting Moving Objects in Video with Sparsity and Connectivity | |
Pan, Jing1,2; Li, Xiaoli1; Li, Xuelong3 ; Pang, Yanwei1
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| 作者部门 | 光学影像学习与分析中心 |
| 2016-06-01 | |
| 发表期刊 | COGNITIVE COMPUTATION
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| ISSN | 1866-9956 |
| 卷号 | 8期号:3页码:420-428 |
| 产权排序 | 3 |
| 摘要 | Moving object detection is crucial for cognitive vision-based robot tasks. However, due to noise, dynamic background, variations in illumination, and high frame rate, it is a challenging task to robustly and efficiently detect moving objects in video using the clue of motion. State-of-the-art batch-based methods view a sequence of images as a whole and then model the background and foreground together with the constraints of foreground sparsity and connectivity (smoothness) in a unified framework. But the efficiency of the batch-based methods is very low. State-of-the-art incremental methods model the background by a subspace whose bases are updated frame by frame. However, such incremental methods do not make full use of the foreground sparsity and connectivity. In this paper, we develop an incremental method for detecting moving objects in video. Compared to existing methods, the proposed method not only incrementally models the subspace for background reconstruction but also takes into account the sparsity and connectivity of the foreground. The optimization of the model is very efficient. Experimental results on nine public videos demonstrate that the proposed method is much efficient than the state-of-the-art batch methods and has higher F1-score than the state-of-the-art incremental methods. |
| 文章类型 | Article |
| 关键词 | Subspace Learning Object Detection Sparsity Connectivity |
| WOS标题词 | Science & Technology ; Technology ; Life Sciences & Biomedicine |
| DOI | 10.1007/s12559-015-9373-5 |
| 收录类别 | SCI ; EI |
| 关键词[WOS] | SURVEILLANCE ; MODEL |
| 语种 | 英语 |
| WOS研究方向 | Computer Science ; Neurosciences & Neurology |
| 项目资助者 | National Basic Research Program of China (973 Program)(2014CB340400) ; National Natural Science Foundation of China(61172121 ; Chinese Academy of Sciences(KGZD-EW-T03) ; Tianjin University of Technology and Education(RC14-46) ; 61271412 ; 61472274 ; 61222109 ; 61503274) |
| WOS类目 | Computer Science, Artificial Intelligence ; Neurosciences |
| WOS记录号 | WOS:000376284900003 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/28136 |
| 专题 | 光谱成像技术研究室 |
| 作者单位 | 1.Tianjin Univ, Sch Elect Informat Engn, Tianjin 300072, Peoples R China 2.Tianjin Univ Technol & Educ, Sch Elect Engn, Tianjin 300222, Peoples R China 3.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China |
| 推荐引用方式 GB/T 7714 | Pan, Jing,Li, Xiaoli,Li, Xuelong,et al. Incrementally Detecting Moving Objects in Video with Sparsity and Connectivity[J]. COGNITIVE COMPUTATION,2016,8(3):420-428. |
| APA | Pan, Jing,Li, Xiaoli,Li, Xuelong,&Pang, Yanwei.(2016).Incrementally Detecting Moving Objects in Video with Sparsity and Connectivity.COGNITIVE COMPUTATION,8(3),420-428. |
| MLA | Pan, Jing,et al."Incrementally Detecting Moving Objects in Video with Sparsity and Connectivity".COGNITIVE COMPUTATION 8.3(2016):420-428. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Incrementally Detect（1525KB） | 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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