中文版 | English
Title

Learning Dual-Fused Modality-Aware Representations for RGBD Tracking

Author
Corresponding AuthorFeng Zheng
DOI
Publication Years
2022-11-15
Conference Name
European Conference on Computer Vision2022
Conference Date
2022/10/23-2022/10/27
Conference Place
特拉维夫
Abstract

Object tracking is to localize an arbitrary object in a video sequence, given only the object description in the first frame. It can be applied in lots of applications in video surveillance, autonomous driving [18,35,23], and robotics [19]. Recent years witness the development of RGBD (RGB+Depth) object tracking thanks to the affordable and accurate depth cameras. RGBD tracking aims to track the objects more robustly and accurately with the help of depth information, even in color-failed scenarios, e.g., target occlusion and dark scenes. Compared to conventional RGB-based tracking, the major difficulty of RGBD S. Gao et al.

Keywords
SUSTech Authorship
First ; Corresponding
Language
English
Data Source
人工提交
Publication Status
在线出版
Citation statistics
Cited Times [WOS]:0
Document TypeConference paper
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/415606
DepartmentDepartment of Computer Science and Engineering
Affiliation
1.Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
2.University of Birmingham, Birmingham, United Kingdom
3.University of Electronic Science and Technology of China , Chengdu, China
First Author AffilicationDepartment of Computer Science and Engineering
Corresponding Author AffilicationDepartment of Computer Science and Engineering
First Author's First AffilicationDepartment of Computer Science and Engineering
Recommended Citation
GB/T 7714
Shang Gao,Jinyu Yang,Zhe Li,et al. Learning Dual-Fused Modality-Aware Representations for RGBD Tracking[C],2022.
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