Recent advances and trends in visual tracking: A review

被引:479
作者
Yang, Hanxuan [2 ,3 ]
Shao, Ling [1 ,2 ]
Zheng, Feng [2 ]
Wang, Liang [4 ]
Song, Zhan [2 ,5 ]
机构
[1] Univ Sheffield, Dept Elect & Elect Engn, Sheffield S10 2TN, S Yorkshire, England
[2] Chinese Acad Sci, Shenzhen Inst Adv Technol, Beijing 100864, Peoples R China
[3] S China Agr Univ, Dept Elect Engn, Guangzhou, Peoples R China
[4] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100864, Peoples R China
[5] Chinese Univ Hong Kong, Hong Kong, Hong Kong, Peoples R China
基金
中国国家自然科学基金;
关键词
Visual tracking; Feature descriptor; Online learning; Contextural information; Monte Carlo sampling; FILTER;
D O I
10.1016/j.neucom.2011.07.024
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The goal of this paper is to review the state-of-the-art progress on visual tracking methods, classify them into different categories, as well as identify future trends. Visual tracking is a fundamental task in many computer vision applications and has been well studied in the last decades. Although numerous approaches have been proposed, robust visual tracking remains a huge challenge. Difficulties in visual tracking can arise due to abrupt object motion, appearance pattern change, non-rigid object structures, occlusion and camera motion. In this paper, we first analyze the state-of-the-art feature descriptors which are used to represent the appearance of tracked objects. Then, we categorize the tracking progresses into three groups, provide detailed descriptions of representative methods in each group, and examine their positive and negative aspects. At last, we outline the future trends for visual tracking research. (C) 2011 Elsevier B.V. All rights reserved.
引用
收藏
页码:3823 / 3831
页数:9
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