Advances in Deep Learning Methods for Visual Tracking: Literature Review and Fundamentals

doi:10.1007/s11633-020-1274-8

CASIA OpenIR > 学术期刊 > Machine Intelligence Research

	Advances in Deep Learning Methods for Visual Tracking: Literature Review and Fundamentals
	Xiao-Qin Zhang; Run-Hua Jiang; Chen-Xiang Fan; Tian-Yu Tong; Tao Wang Peng-Cheng Huang
发表期刊	International Journal of Automation and Computing
ISSN	1476-8186
	2021
卷号	18 期号:3 页码:311-333
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.
关键词	Deep learning visual tracking data-invariant data-adaptive general components
DOI	10.1007/s11633-020-1274-8
引用统计	被引频次：13[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/44286
专题	学术期刊_Machine Intelligence Research
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.

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