Feature Extraction with Refinement and Rebuilding Module for Visual Tracking Article Swipe
Wenshuang Zhang
,
Hanhao Li
,
Pengcheng Sha
,
Dezheng Zhang
,
Jun Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-6521857/v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-6521857/v1
Convolutional neural network based trackers have achieved excellent tracking performance in terms of accuracy and speed. The feature extraction network is an essential component of trackers. However, existing feature extraction sub-networks do not remove redundant spatial and channel information. There is significant redundancy in deep neural networks, not only in model parameters but also in the spatial and channel dimensions of feature maps. However, existing methods only reduce redundancy in either the channel or spatial dimension. As a result, redundancy issues in neural networks remain unresolved. In this work, we design a feature extraction subnetwork with a refinement and rebuilding module. Spatial and channel feature information is fully utilized to obtain more accurate target location information for the target template and search region, and also highlights the foreground information and suppresses background information. The template branch and search branch use weight separation to remove redundant features and reconstruct the remained features. This suppresses redundancy in the spatial dimension and enhances feature representation. A split transformation and fusion strategy is employed to reduce redundancy in the channel dimension as well as computational cost and storage. We propose a new tracking framework with Spatial Feature Refinement Module and the Channel Feature Rebuilding Module. We evaluated the proposed tracker on LaSOT, TrackingNet, NFS, UAV123, GOT-10K and TNL2K benchmarks, achieving leading performance with a tracking speed of 105 FPS.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-6521857/v1
- https://www.researchsquare.com/article/rs-6521857/latest.pdf
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410185606
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410185606Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-6521857/v1Digital Object Identifier
- Title
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Feature Extraction with Refinement and Rebuilding Module for Visual TrackingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-05-06Full publication date if available
- Authors
-
Wenshuang Zhang, Hanhao Li, Pengcheng Sha, Dezheng Zhang, Jun WangList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-6521857/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-6521857/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-6521857/latest.pdfDirect OA link when available
- Concepts
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Tracking (education), Artificial intelligence, Computer science, Computer vision, Extraction (chemistry), Feature (linguistics), Feature extraction, Pattern recognition (psychology), Psychology, Chromatography, Chemistry, Philosophy, Pedagogy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Spatial | 102, 192 |
| abstract_inverted_index.UAV123, | 211 |
| abstract_inverted_index.channel | 38, 59, 73, 104, 176 |
| abstract_inverted_index.feature | 18, 29, 62, 93, 105, 161 |
| abstract_inverted_index.leading | 217 |
| abstract_inverted_index.methods | 66 |
| abstract_inverted_index.module. | 101 |
| abstract_inverted_index.network | 3, 20 |
| abstract_inverted_index.propose | 186 |
| abstract_inverted_index.region, | 123 |
| abstract_inverted_index.result, | 79 |
| abstract_inverted_index.spatial | 36, 57, 75, 157 |
| abstract_inverted_index.tracker | 206 |
| abstract_inverted_index.However, | 27, 64 |
| abstract_inverted_index.accuracy | 14 |
| abstract_inverted_index.accurate | 113 |
| abstract_inverted_index.achieved | 7 |
| abstract_inverted_index.employed | 170 |
| abstract_inverted_index.enhances | 160 |
| abstract_inverted_index.existing | 28, 65 |
| abstract_inverted_index.features | 146 |
| abstract_inverted_index.location | 115 |
| abstract_inverted_index.networks | 84 |
| abstract_inverted_index.proposed | 205 |
| abstract_inverted_index.remained | 150 |
| abstract_inverted_index.storage. | 184 |
| abstract_inverted_index.strategy | 168 |
| abstract_inverted_index.template | 120, 135 |
| abstract_inverted_index.trackers | 5 |
| abstract_inverted_index.tracking | 9, 189, 221 |
| abstract_inverted_index.utilized | 109 |
| abstract_inverted_index.achieving | 216 |
| abstract_inverted_index.component | 24 |
| abstract_inverted_index.dimension | 158, 177 |
| abstract_inverted_index.essential | 23 |
| abstract_inverted_index.evaluated | 203 |
| abstract_inverted_index.excellent | 8 |
| abstract_inverted_index.features. | 151 |
| abstract_inverted_index.framework | 190 |
| abstract_inverted_index.networks, | 47 |
| abstract_inverted_index.redundant | 35, 145 |
| abstract_inverted_index.trackers. | 26 |
| abstract_inverted_index.Rebuilding | 200 |
| abstract_inverted_index.Refinement | 194 |
| abstract_inverted_index.background | 132 |
| abstract_inverted_index.dimension. | 76 |
| abstract_inverted_index.dimensions | 60 |
| abstract_inverted_index.extraction | 19, 30, 94 |
| abstract_inverted_index.foreground | 128 |
| abstract_inverted_index.highlights | 126 |
| abstract_inverted_index.parameters | 52 |
| abstract_inverted_index.rebuilding | 100 |
| abstract_inverted_index.redundancy | 43, 69, 80, 154, 173 |
| abstract_inverted_index.refinement | 98 |
| abstract_inverted_index.separation | 142 |
| abstract_inverted_index.subnetwork | 95 |
| abstract_inverted_index.suppresses | 131, 153 |
| abstract_inverted_index.benchmarks, | 215 |
| abstract_inverted_index.information | 106, 116, 129 |
| abstract_inverted_index.performance | 10, 218 |
| abstract_inverted_index.reconstruct | 148 |
| abstract_inverted_index.significant | 42 |
| abstract_inverted_index.unresolved. | 86 |
| abstract_inverted_index.TrackingNet, | 209 |
| abstract_inverted_index.information. | 39, 133 |
| abstract_inverted_index.sub-networks | 31 |
| abstract_inverted_index.Convolutional | 1 |
| abstract_inverted_index.computational | 181 |
| abstract_inverted_index.transformation | 165 |
| abstract_inverted_index.representation. | 162 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.15092798 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |