Survey of single-target visual tracking methods based on online learning

被引：20
作者：
Liu, Qi [1 ]
Zhao, Xiaoguang [1 ]
Hou, Zengguang [1 ]
机构：
[1] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100080, Peoples R China
来源：
IET COMPUTER VISION | 2014年 / 8卷 / 05期
关键词：
OBJECT TRACKING; DISCRIMINATIVE TRACKING; ROBUST; RECOGNITION; SUBSPACE; MODELS; VIDEO;
D O I：
10.1049/iet-cvi.2013.0134
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Visual tracking is a popular and challenging topic in computer vision and robotics. Owing to changes in the appearance of the target and complicated variations that may occur in various scenes, online learning scheme is necessary for advanced visual tracking framework to adopt. This paper briefly introduces the challenges and applications of visual tracking and focuses on discussing the state-of-the-art online-learning-based tracking methods by category. We provide detail descriptions of representative methods in each category, and examine their pros and cons. Moreover, several most representative algorithms are implemented to provide quantitative reference. At last, we outline several trends for future visual tracking research.
引用
收藏
页码：419 / 428
页数：10
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