Deep Spatial and Temporal Network for Robust Visual Object Tracking
文献类型：期刊论文
| 作者 | Teng, Zhu1; Xing, Junliang2 ; Wang, Qiang2; Zhang, Baopeng1; Fan, Jianping3
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| 刊名 | IEEE TRANSACTIONS ON IMAGE PROCESSING
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| 出版日期 | 2020 |
| 卷号 | 29页码:1762-1775 |
| 关键词 | Target tracking Visualization Biological system modeling Correlation Training Benchmark testing Visual tracking deep network spatial-temporal LSTM |
| ISSN号 | 1057-7149 |
| DOI | 10.1109/TIP.2019.2942502 |
| 通讯作者 | Xing, Junliang(jlxing@nlpr.ia.ac.cn) |
| 英文摘要 | There are two key components that can be leveraged for visual tracking: (a) object appearances; and (b) object motions. Many existing techniques have recently employed deep learning to enhance visual tracking due to its superior representation power and strong learning ability, where most of them employed object appearances but few of them exploited object motions. In this work, a deep spatial and temporal network (DSTN) is developed for visual tracking by explicitly exploiting both the object representations from each frame and their dynamics along multiple frames in a video, such that it can seamlessly integrate the object appearances with their motions to produce compact object appearances and capture their temporal variations effectively. Our DSTN method, which is deployed into a tracking pipeline in a coarse-to-fine form, can perceive the subtle differences on spatial and temporal variations of the target (object being tracked), and thus it benefits from both off-line training and online fine-tuning. We have also conducted our experiments over four largest tracking benchmarks, including OTB-2013, OTB-2015, VOT2015, and VOT2017, and our experimental results have demonstrated that our DSTN method can achieve competitive performance as compared with the state-of-the-art techniques. The source code, trained models, and all the experimental results of this work will be made public available to facilitate further studies on this problem. |
| 资助项目 | Natural Science Foundation of China[61972027] ; Natural Science Foundation of China[61672519] ; Natural Science Foundation of China[61872035] ; Fundamental Research Funds for the Central Universities of China[2019JBM022] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000501324900008 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/29339]  |
| 专题 | 智能系统与工程 |
| 通讯作者 | Xing, Junliang |
| 作者单位 | 1.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Univ North Carolina Charlotte, Dept Comp Sci, Charlotte, NC 28223 USA |
| 推荐引用方式 GB/T 7714 | Teng, Zhu,Xing, Junliang,Wang, Qiang,et al. Deep Spatial and Temporal Network for Robust Visual Object Tracking[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2020,29:1762-1775. |
| APA | Teng, Zhu,Xing, Junliang,Wang, Qiang,Zhang, Baopeng,&Fan, Jianping.(2020).Deep Spatial and Temporal Network for Robust Visual Object Tracking.IEEE TRANSACTIONS ON IMAGE PROCESSING,29,1762-1775. |
| MLA | Teng, Zhu,et al."Deep Spatial and Temporal Network for Robust Visual Object Tracking".IEEE TRANSACTIONS ON IMAGE PROCESSING 29(2020):1762-1775. |
入库方式： OAI收割
来源：自动化研究所
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