Adaptive multi-branch correlation filters for robust visual tracking
文献类型：期刊论文
| 作者 | Li, Xiaojing2; Huang, Lei1,2; Wei, Zhiqiang1,2; Nie, Jie2; Chen, Zhineng3
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| 刊名 | NEURAL COMPUTING & APPLICATIONS
 |
| 出版日期 | 2020-08-12 |
| 页码 | 16 |
| 关键词 | Visual tracking Correlation filter Multi-branch Appearance changes Background suppression |
| ISSN号 | 0941-0643 |
| DOI | 10.1007/s00521-020-05126-9 |
| 通讯作者 | Wei, Zhiqiang(weizhiqiang@ouc.edu.cn) |
| 英文摘要 | In recent years, deep convolutional features have been applied to discriminative correlation filters-based methods, which have achieved impressive performance in tracking. Most of them utilize hierarchical features from a certain layer. However, this is not always sufficient to learn target appearance changes and to suppress the background interference in complicated interfering factors (e.g., deformation, fast motion, low resolution, and rotations). In this paper, we propose an adaptive multi-branch correlation filter tracking method, by constructing multi-branch models and using an adaptive selection strategy to improve the accuracy and robustness of visual tracking. Specially, the multi-branch models are introduced to tolerate temporal changes of the object, which can serve different circumstances. In addition, the adaptive selection strategy incorporates both foreground and background information to learn background suppression. To further improve the tracking performance, we propose a measurement method to handle tracking failures from unreliable samples. Extensive experiments on OTB-2013, OTB-2015, and VOT-2016 datasets show that the proposed tracker has comparable performance compared to state-of-the-art tracking methods. Especially, on the OTB-2015, our method significantly improves the baseline with a gain of 5.5% in overlap precision. |
| WOS关键词 | OBJECT TRACKING |
| 资助项目 | National Natural Science Foundation of China[61872326] ; National Natural Science Foundation of China[61672475] ; National Natural Science Foundation of China[61772526] ; Shandong Provincial Natural Science Foundation[ZR2019MF044] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000559302200001 |
| 出版者 | SPRINGER LONDON LTD |
| 资助机构 | National Natural Science Foundation of China ; Shandong Provincial Natural Science Foundation |
| 源URL | [http://ir.ia.ac.cn/handle/173211/40431]  |
| 专题 | 数字内容技术与服务研究中心_远程智能医疗 |
| 通讯作者 | Wei, Zhiqiang |
| 作者单位 | 1.Qingdao Natl Lab Marine Sci & Technol, Qingdao 266000, Peoples R China 2.Ocean Univ China, Coll Informat Sci & Engn, Qingdao 266000, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Xiaojing,Huang, Lei,Wei, Zhiqiang,et al. Adaptive multi-branch correlation filters for robust visual tracking[J]. NEURAL COMPUTING & APPLICATIONS,2020:16. |
| APA | Li, Xiaojing,Huang, Lei,Wei, Zhiqiang,Nie, Jie,&Chen, Zhineng.(2020).Adaptive multi-branch correlation filters for robust visual tracking.NEURAL COMPUTING & APPLICATIONS,16. |
| MLA | Li, Xiaojing,et al."Adaptive multi-branch correlation filters for robust visual tracking".NEURAL COMPUTING & APPLICATIONS (2020):16. |
入库方式： OAI收割
来源：自动化研究所
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