CBFF-Net: A New Framework for Efficient and Accurate Hyperspectral Object Tracking

Gao, L; Liu, P; Jiang, Y; Xie, W; Lei, J; Li, Y; Du, Q

CBFF-Net: A New Framework for Efficient and Accurate Hyperspectral Object Tracking

Gao, L Liu, P Jiang, Y Xie, W Lei, J

Li, Y Du, Q

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Geoscience and Remote Sensing, 2023, 61
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Gao, L
dc.contributor.author	Liu, P
dc.contributor.author	Jiang, Y
dc.contributor.author	Xie, W
dc.contributor.author	Lei, J https://orcid.org/0000-0003-0851-6565
dc.contributor.author	Li, Y
dc.contributor.author	Du, Q
dc.date.accessioned	2024-04-29T09:21:39Z
dc.date.available	2024-04-29T09:21:39Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Transactions on Geoscience and Remote Sensing, 2023, 61
dc.identifier.issn	0196-2892
dc.identifier.issn	1558-0644
dc.identifier.uri	http://hdl.handle.net/10453/178480
dc.description.abstract	Visual object tracking is a fundamental task in computer vision and thrived in recent decades. With the development of snapshot hyperspectral (HS) sensors, efforts have been made to exploit tracking the object with HS videos to overcome the inherent limitation of red-green-blue (RGB) images. Existing HS tracking algorithms extract the deep features from image data separately, which break the interaction information between bands. Therefore, the discrimination ability of HS trackers is limited and the efficiency of the existing HS algorithms is low. In this article, a novel algorithm [cross-band feature fusion network (CBFF-Net)] is proposed for HS object tracking to improve the discrimination ability and reduce the computational complexity. Specifically, the backbone and head network are implemented with modules of a transferred RGB object tracking network to carry out the HS target tracking task while maintaining the discrimination ability learned from RGB data. Moreover, a bidirectional multiple deep feature fusion (BMDFF) module is proposed to fuse the features extracted from different bands of the HS images, and a cross-band group attention (CBGA) module is introduced to learn interaction information across bands of the HS images. Experiments results indicate the superiority in performance of CBFF-Net, and it runs at 24 frames/s (FPS).
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Geoscience and Remote Sensing
dc.relation.isbasedon	10.1109/TGRS.2023.3253173
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0404 Geophysics, 0906 Electrical and Electronic Engineering, 0909 Geomatic Engineering
dc.subject.classification	Geological & Geomatics Engineering
dc.subject.classification	37 Earth sciences
dc.subject.classification	40 Engineering
dc.title	CBFF-Net: A New Framework for Efficient and Accurate Hyperspectral Object Tracking
dc.type	Journal Article
utslib.citation.volume	61
utslib.for	0404 Geophysics
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0909 Geomatic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-04-29T09:21:10Z
pubs.publication-status	Published
pubs.volume	61

Abstract:

Visual object tracking is a fundamental task in computer vision and thrived in recent decades. With the development of snapshot hyperspectral (HS) sensors, efforts have been made to exploit tracking the object with HS videos to overcome the inherent limitation of red-green-blue (RGB) images. Existing HS tracking algorithms extract the deep features from image data separately, which break the interaction information between bands. Therefore, the discrimination ability of HS trackers is limited and the efficiency of the existing HS algorithms is low. In this article, a novel algorithm [cross-band feature fusion network (CBFF-Net)] is proposed for HS object tracking to improve the discrimination ability and reduce the computational complexity. Specifically, the backbone and head network are implemented with modules of a transferred RGB object tracking network to carry out the HS target tracking task while maintaining the discrimination ability learned from RGB data. Moreover, a bidirectional multiple deep feature fusion (BMDFF) module is proposed to fuse the features extracted from different bands of the HS images, and a cross-band group attention (CBGA) module is introduced to learn interaction information across bands of the HS images. Experiments results indicate the superiority in performance of CBFF-Net, and it runs at 24 frames/s (FPS).

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http://hdl.handle.net/10453/178480