Visual Object Tracking in RGB-D Data via Genetic Feature Learning
Joint Authors
Jiang, Ming-Xin
Abdalla, Ahmed N.
Luo, Xian-xian
Hai, Tao
Wang, Hai-yan
Yang, Song
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-05-02
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Visual object tracking is a fundamental component in many computer vision applications.
Extracting robust features of object is one of the most important steps in tracking.
As trackers, only formulated on RGB data, are usually affected by occlusions, appearance, or illumination variations, we propose a novel RGB-D tracking method based on genetic feature learning in this paper.
Our approach addresses feature learning as an optimization problem.
As owning the advantage of parallel computing, genetic algorithm (GA) has fast speed of convergence and excellent global optimization performance.
At the same time, unlike handcrafted feature and deep learning methods, GA can be employed to solve the problem of feature representation without prior knowledge, and it has no use for a large number of parameters to be learned.
The candidate solution in RGB or depth modality is represented as an encoding of an image in GA, and genetic feature is learned through population initialization, fitness evaluation, selection, crossover, and mutation.
The proposed RGB-D tracker is evaluated on popular benchmark dataset, and experimental results indicate that our method achieves higher accuracy and faster tracking speed.
American Psychological Association (APA)
Jiang, Ming-Xin& Luo, Xian-xian& Hai, Tao& Wang, Hai-yan& Yang, Song& Abdalla, Ahmed N.. 2019. Visual Object Tracking in RGB-D Data via Genetic Feature Learning. Complexity،Vol. 2019, no. 2019, pp.1-8.
https://search.emarefa.net/detail/BIM-1131826
Modern Language Association (MLA)
Jiang, Ming-Xin…[et al.]. Visual Object Tracking in RGB-D Data via Genetic Feature Learning. Complexity No. 2019 (2019), pp.1-8.
https://search.emarefa.net/detail/BIM-1131826
American Medical Association (AMA)
Jiang, Ming-Xin& Luo, Xian-xian& Hai, Tao& Wang, Hai-yan& Yang, Song& Abdalla, Ahmed N.. Visual Object Tracking in RGB-D Data via Genetic Feature Learning. Complexity. 2019. Vol. 2019, no. 2019, pp.1-8.
https://search.emarefa.net/detail/BIM-1131826
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1131826