Open AccessSurvey of single‐target visual tracking methods based on online learning
Author(s) -
Liu Qi,
Zhao Xiaoguang,
Hou Zengguang
Publication year - 2014
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2013.0134
Subject(s) - computer science , artificial intelligence , tracking (education) , eye tracking , computer vision , online learning , scheme (mathematics) , visualization , robotics , machine learning , multimedia , robot , psychology , pedagogy , mathematical analysis , mathematics
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.