A Fast Object Tracker Based on Integrated Multiple Features and Dynamic Learning Rate
Joint Authors
Li, Feng
Zhang, Jianming
Wang, Jin
Wu, You
Jin, Xiaokang
Source
Mathematical Problems in Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-12-24
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Object tracking is a vital topic in computer vision.
Although tracking algorithms have gained great development in recent years, its robustness and accuracy still need to be improved.
In this paper, to overcome single feature with poor representation ability in a complex image sequence, we put forward a multifeature integration framework, including the gray features, Histogram of Gradient (HOG), color-naming (CN), and Illumination Invariant Features (IIF), which effectively improve the robustness of object tracking.
In addition, we propose a model updating strategy and introduce a skewness to measure the confidence degree of tracking result.
Unlike previous tracking algorithms, we judge the relationship of skewness values between two adjacent frames to decide the updating of target appearance model to use a dynamic learning rate.
This way makes our tracker further improve the robustness of tracking and effectively prevents the target drifting caused by occlusion and deformation.
Extensive experiments on large-scale benchmark containing 50 image sequences show that our tracker is better than most existing excellent trackers in tracking performance and can run at average speed over 43 fps.
American Psychological Association (APA)
Zhang, Jianming& Wu, You& Jin, Xiaokang& Li, Feng& Wang, Jin. 2018. A Fast Object Tracker Based on Integrated Multiple Features and Dynamic Learning Rate. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1208148
Modern Language Association (MLA)
Zhang, Jianming…[et al.]. A Fast Object Tracker Based on Integrated Multiple Features and Dynamic Learning Rate. Mathematical Problems in Engineering No. 2018 (2018), pp.1-14.
https://search.emarefa.net/detail/BIM-1208148
American Medical Association (AMA)
Zhang, Jianming& Wu, You& Jin, Xiaokang& Li, Feng& Wang, Jin. A Fast Object Tracker Based on Integrated Multiple Features and Dynamic Learning Rate. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1208148
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1208148