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Survey of single-target visual tracking methods based on online learning
by
Hou, Zengguang
, Zhao, Xiaoguang
, Liu, Qi
in
advanced visual tracking framework
/ Categories
/ Computer vision
/ Descriptions
/ Distance education
/ Distance learning
/ learning (artificial intelligence)
/ object tracking
/ online learning scheme
/ online-learning-based tracking method
/ robot vision
/ robotics
/ single-target visual tracking method
/ State of the art
/ target appearance
/ Tracking
/ Visual
2014
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Survey of single-target visual tracking methods based on online learning
by
Hou, Zengguang
, Zhao, Xiaoguang
, Liu, Qi
in
advanced visual tracking framework
/ Categories
/ Computer vision
/ Descriptions
/ Distance education
/ Distance learning
/ learning (artificial intelligence)
/ object tracking
/ online learning scheme
/ online-learning-based tracking method
/ robot vision
/ robotics
/ single-target visual tracking method
/ State of the art
/ target appearance
/ Tracking
/ Visual
2014
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Do you wish to request the book?
Survey of single-target visual tracking methods based on online learning
by
Hou, Zengguang
, Zhao, Xiaoguang
, Liu, Qi
in
advanced visual tracking framework
/ Categories
/ Computer vision
/ Descriptions
/ Distance education
/ Distance learning
/ learning (artificial intelligence)
/ object tracking
/ online learning scheme
/ online-learning-based tracking method
/ robot vision
/ robotics
/ single-target visual tracking method
/ State of the art
/ target appearance
/ Tracking
/ Visual
2014
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Survey of single-target visual tracking methods based on online learning
Journal Article
Survey of single-target visual tracking methods based on online learning
2014
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Overview
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.
Publisher
The Institution of Engineering and Technology,John Wiley & Sons, Inc,Wiley
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