Consistent multi-layer subtask tracker via hyper-graph regularization
文献类型：期刊论文
| 作者 | Fan BJ(范保杰) ; Cong Y(丛杨)
|
| 刊名 | Pattern Recognition
 |
| 出版日期 | 2017 |
| 卷号 | 67页码:299-312 |
| 关键词 | Multi-layer subtask learning Intrinsic geometrical structure Graph regularization Normalized collaborate metric Object tracking |
| ISSN号 | 0031-3203 |
| 产权排序 | 2 |
| 通讯作者 | Fan BJ(范保杰) |
| 中文摘要 | Most multi-task learning based trackers adopt similar task definition by assuming that all tasks share a common feature set, which can't cover the real situation well. In this paper, we define the subtasks from the novel perspective, and develop a structured and consistent multi-layer multi-subtask tracker with graph regularization. The tracking task is completed by the collaboration of multi-layer subtasks. Different subtasks correspond to the tracking of different parts in the target area. The correspondences of the subtasks among the adjacent frames are consistent and smooth. The proposed model introduces hyper-graph regularizer to preserve the global and local intrinsic geometrical structures among and inside target candidates or trained samples, and decomposes the representative matrix of the subtasks into two components: low-rank property captures the subtask relationship, group-sparse property identifies the outlier subtasks. Moreover, a collaborate metric scheme is developed to find the best candidate, by concerning both discrimination reliability and representation accuracy. We show that the proposed multi-layer multi-subtask learning based tracker is a general model, which accommodates most existing multi-task trackers with the respective merits. Encouraging experimental results on a large set of public video sequences justify the effectiveness and robustness of the proposed tracker, and achieve comparable performance against many state-of-the-art methods. |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000399520700025 |
| 源URL | [http://ir.sia.cn/handle/173321/21226]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 作者单位 | 1.State Key Laboratory of Robotics, Chinese Academy of Sciences, China 2.Automation College, Nanjing University of Posts and Telecommunications, China |
| 推荐引用方式 GB/T 7714 | Fan BJ,Cong Y. Consistent multi-layer subtask tracker via hyper-graph regularization[J]. Pattern Recognition,2017,67:299-312. |
| APA | Fan BJ,&Cong Y.(2017).Consistent multi-layer subtask tracker via hyper-graph regularization.Pattern Recognition,67,299-312. |
| MLA | Fan BJ,et al."Consistent multi-layer subtask tracker via hyper-graph regularization".Pattern Recognition 67(2017):299-312. |
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。