Efficient and Practical Correlation Filter Tracking
文献类型：期刊论文
| 作者 | Zhu, Chengfei1 ; Jiang, Shan1,2; Li, Shuxiao1,2; Lan, Xiaosong1
|
| 刊名 | SENSORS
 |
| 出版日期 | 2021-02-01 |
| 卷号 | 21期号:3页码:17 |
| 关键词 | visual tracking correlation filter model update long-term tracking |
| DOI | 10.3390/s21030790 |
| 通讯作者 | Lan, Xiaosong(xiaosonglan@gmail.com) |
| 英文摘要 | Visual tracking is a basic task in many applications. However, the heavy computation and low speed of many recent trackers limit their applications in some computing power restricted scenarios. On the other hand, the simple update scheme of most correlation filter-based trackers restricts their robustness during target deformation and occlusion. In this paper, we explore the update scheme of correlation filter-based trackers and propose an efficient and adaptive training sample update scheme. The training sample extracted in each frame is updated to the training set according to its distance between existing samples measured with a difference hashing algorithm or discarded according to tracking result reliability. In addition, we expand our new tracker to long-term tracking. On the basis of the proposed model updating mechanism, we propose a new tracking state discrimination mechanism to accurately judge tracking failure, and resume tracking after the target is recovered. Experiments on OTB-2015, Temple Color 128 and UAV123 (including UAV20L) demonstrate that our tracker performs favorably against state-of-the-art trackers with light computation and runs over 100 fps on desktop computer with Intel i7-8700 CPU(3.2 GHz). |
| 资助项目 | National Natural Science Foundation of China[U19B2033] ; National Natural Science Foundation of China[62076020] ; National Key RD Program[2019YFF0301801] ; Frontier Science and Technology Innovation Project[2019QY2404] ; Innovation Academy for Light-Duty Gas Turbine, Chinese Academy of Sciences[CXYJJ19-ZD-02] |
| WOS研究方向 | Chemistry ; Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:000615501000001 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China ; National Key RD Program ; Frontier Science and Technology Innovation Project ; Innovation Academy for Light-Duty Gas Turbine, Chinese Academy of Sciences |
| 源URL | [http://ir.ia.ac.cn/handle/173211/43093]  |
| 专题 | 综合信息系统研究中心_脑机融合与认知评估 |
| 通讯作者 | Lan, Xiaosong |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhu, Chengfei,Jiang, Shan,Li, Shuxiao,et al. Efficient and Practical Correlation Filter Tracking[J]. SENSORS,2021,21(3):17. |
| APA | Zhu, Chengfei,Jiang, Shan,Li, Shuxiao,&Lan, Xiaosong.(2021).Efficient and Practical Correlation Filter Tracking.SENSORS,21(3),17. |
| MLA | Zhu, Chengfei,et al."Efficient and Practical Correlation Filter Tracking".SENSORS 21.3(2021):17. |
入库方式： OAI收割
来源：自动化研究所
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