Advances in Deep Learning Methods for Visual Tracking: Literature Review and Fundamentals
文献类型：期刊论文
| 作者 | Xiao-Qin Zhang; Run-Hua Jiang; Chen-Xiang Fan; Tian-Yu Tong; Tao Wang Peng-Cheng Huang |
| 刊名 | International Journal of Automation and Computing
 |
| 出版日期 | 2021 |
| 卷号 | 18期号:3页码:311-333 |
| 关键词 | Deep learning visual tracking data-invariant data-adaptive general components |
| ISSN号 | 1476-8186 |
| DOI | 10.1007/s11633-020-1274-8 |
| 英文摘要 | Recently, deep learning has achieved great success in visual tracking tasks, particularly in single-object tracking. This paper provides a comprehensive review of state-of-the-art single-object tracking algorithms based on deep learning. First, we introduce basic knowledge of deep visual tracking, including fundamental concepts, existing algorithms, and previous reviews. Second, we briefly review existing deep learning methods by categorizing them into data-invariant and data-adaptive methods based on whether they can dynamically change their model parameters or architectures. Then, we conclude with the general components of deep trackers. In this way, we systematically analyze the novelties of several recently proposed deep trackers. Thereafter, popular datasets such as Object Tracking Benchmark (OTB) and Visual Object Tracking (VOT) are discussed, along with the performances of several deep trackers. Finally, based on observations and experimental results, we discuss three different characteristics of deep trackers, i.e., the relationships between their general components, exploration of more effective tracking frameworks, and interpretability of their motion estimation components. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/44286]  |
| 专题 | 自动化研究所_学术期刊_International Journal of Automation and Computing |
| 作者单位 | College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China |
| 推荐引用方式 GB/T 7714 | Xiao-Qin Zhang,Run-Hua Jiang,Chen-Xiang Fan,et al. Advances in Deep Learning Methods for Visual Tracking: Literature Review and Fundamentals[J]. International Journal of Automation and Computing,2021,18(3):311-333. |
| APA | Xiao-Qin Zhang,Run-Hua Jiang,Chen-Xiang Fan,Tian-Yu Tong,&Tao Wang Peng-Cheng Huang.(2021).Advances in Deep Learning Methods for Visual Tracking: Literature Review and Fundamentals.International Journal of Automation and Computing,18(3),311-333. |
| MLA | Xiao-Qin Zhang,et al."Advances in Deep Learning Methods for Visual Tracking: Literature Review and Fundamentals".International Journal of Automation and Computing 18.3(2021):311-333. |
入库方式： OAI收割
来源：自动化研究所
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