HCDC-SRCF tracker: Learning an adaptively multi-feature fuse tracker in spatial regularized correlation filters framework

Liu, B; Chang, X; Yuan, D; Yang, Y

HCDC-SRCF tracker: Learning an adaptively multi-feature fuse tracker in spatial regularized correlation filters framework

Liu, B Chang, X

Yuan, D Yang, Y

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Knowledge-Based Systems, 2022, 238
Issue Date:: 2022-02-28

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Field	Value	Language
dc.contributor.author	Liu, B
dc.contributor.author	Chang, X https://orcid.org/0000-0002-7778-8807
dc.contributor.author	Yuan, D
dc.contributor.author	Yang, Y
dc.date.accessioned	2023-03-31T10:15:08Z
dc.date.available	2023-03-31T10:15:08Z
dc.date.issued	2022-02-28
dc.identifier.citation	Knowledge-Based Systems, 2022, 238
dc.identifier.issn	0950-7051
dc.identifier.issn	1872-7409
dc.identifier.uri	http://hdl.handle.net/10453/168977
dc.description.abstract	Integrating multi-feature based on multi-layer features from the convolutional network or based on multiple hand-crafted features has been proved to be an effective way for improving tracking performance. In this work, we investigate how to integrate multi-layer convolutional features with hand-crafted features. Specifically, an adaptive multi-feature fusion strategy is proposed based on convolutional features from ResNet-101 and hand-crafted features from HOG as well as Grayscale in spatial regularized correlation filter framework. We fully consider the complementary advantages of multi-layer convolutional features and hand-crafted features to construct a robust and reliable appearance representation of the target. Comprehensive experimental results on benchmark datasets demonstrate that our tracker has achieved significant performance improvements in various challenging environments. Compared to the trackers based only on multi-layer convolutional features or complete hand-crafted fusion features, the most important is that our proposed tracker obtains more competitive tracking performance. Our tracker is publicly available. You can find open-sourced code of our tracker at https://github.com/binger1225/HCDC-SRCF.
dc.language	English
dc.publisher	ELSEVIER
dc.relation	http://purl.org/au-research/grants/arc/DE190100626
dc.relation.ispartof	Knowledge-Based Systems
dc.relation.isbasedon	10.1016/j.knosys.2021.107913
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	08 Information and Computing Sciences, 15 Commerce, Management, Tourism and Services, 17 Psychology and Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	HCDC-SRCF tracker: Learning an adaptively multi-feature fuse tracker in spatial regularized correlation filters framework
dc.type	Journal Article
utslib.citation.volume	238
utslib.for	08 Information and Computing Sciences
utslib.for	15 Commerce, Management, Tourism and Services
utslib.for	17 Psychology and Cognitive Sciences
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/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	in_progress	*
dc.date.updated	2023-03-31T10:15:05Z
pubs.publication-status	Published
pubs.volume	238

Abstract:

Integrating multi-feature based on multi-layer features from the convolutional network or based on multiple hand-crafted features has been proved to be an effective way for improving tracking performance. In this work, we investigate how to integrate multi-layer convolutional features with hand-crafted features. Specifically, an adaptive multi-feature fusion strategy is proposed based on convolutional features from ResNet-101 and hand-crafted features from HOG as well as Grayscale in spatial regularized correlation filter framework. We fully consider the complementary advantages of multi-layer convolutional features and hand-crafted features to construct a robust and reliable appearance representation of the target. Comprehensive experimental results on benchmark datasets demonstrate that our tracker has achieved significant performance improvements in various challenging environments. Compared to the trackers based only on multi-layer convolutional features or complete hand-crafted fusion features, the most important is that our proposed tracker obtains more competitive tracking performance. Our tracker is publicly available. You can find open-sourced code of our tracker at https://github.com/binger1225/HCDC-SRCF.

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http://hdl.handle.net/10453/168977