Multimodal Deep Feature Fusion (MMDFF) for RGB-D Tracking
Joint Authors
Jiang, Ming-Xin
Deng, Chao
Zhang, Ming-min
Shan, Jing-song
Zhang, Haiyan
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-11-28
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Visual tracking is still a challenging task due to occlusion, appearance changes, complex motion, etc.
We propose a novel RGB-D tracker based on multimodal deep feature fusion (MMDFF) in this paper.
MMDFF model consists of four deep Convolutional Neural Networks (CNNs): Motion-specific CNN, RGB- specific CNN, Depth-specific CNN, and RGB-Depth correlated CNN.
The depth image is encoded into three channels which are sent into depth-specific CNN to extract deep depth features.
The optical flow image is calculated for every frame and then is fed to motion-specific CNN to learn deep motion features.
Deep RGB, depth, and motion information can be effectively fused at multiple layers via MMDFF model.
Finally, multimodal fusion deep features are sent into the C-COT tracker to obtain the tracking result.
For evaluation, experiments are conducted on two recent large-scale RGB-D datasets and results demonstrate that our proposed RGB-D tracking method achieves better performance than other state-of-art RGB-D trackers.
American Psychological Association (APA)
Jiang, Ming-Xin& Deng, Chao& Zhang, Ming-min& Shan, Jing-song& Zhang, Haiyan. 2018. Multimodal Deep Feature Fusion (MMDFF) for RGB-D Tracking. Complexity،Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1134733
Modern Language Association (MLA)
Jiang, Ming-Xin…[et al.]. Multimodal Deep Feature Fusion (MMDFF) for RGB-D Tracking. Complexity No. 2018 (2018), pp.1-8.
https://search.emarefa.net/detail/BIM-1134733
American Medical Association (AMA)
Jiang, Ming-Xin& Deng, Chao& Zhang, Ming-min& Shan, Jing-song& Zhang, Haiyan. Multimodal Deep Feature Fusion (MMDFF) for RGB-D Tracking. Complexity. 2018. Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1134733
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1134733