Learning Target-Oriented Dual Attention for Robust RGB-T Tracking
Open accessAbstract
RGB-Thermal object tracking attempts to locate target object using complementary visual and thermal infrared data. Existing RGB-T trackers fuse different modalities by robust feature representation learning or adaptive modal weighting. However, how to integrate dual attention mechanism for visual tracking is still a subject that has not been studied yet. In this paper, we propose two visual attention mechanisms for robust RGB-T object tracking. Specifically, the local attention is implemented by exploiting the common visual attention of RGB and thermal data to train deep classifiers. We also introduce the global attention, which is a multimodal target-driven attention estimation network. It can provide global proposals for the classifier together with local proposals extracted from previous tracking result. Extensive experiments on two RGB-T benchmark datasets validated the effectiveness of our proposed algorithm.
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Several multimodal variants of multi-object tracking have been proposed to improve the speed and accuracy of visual tracking by using multiple data sources.
“To date, several multimodal variants of MOT have been proposed to improve the speed and accuracy of visual tracking by using multiple data sources, e.g., thermal, shortwave infrared, and hyperspectral data [195], RGB and thermal data [196], RGB and infrared data [197], etc.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [196]
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- Authors
- Yang, R., Zhu, Y., Wang, X., Li, C., & Tang, J.
- Affiliations
- Anhui University (CN)
- Venue
- 2019 IEEE International Conference on Image Processing (ICIP)
- Publisher
- IEEE
- Published
- 2019-09
- Pages
- 3975-3979
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W2970995493
- Cited by
- 45
- References
- 22
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 7 · 2023: 12 · 2022: 8 · 2021: 11 · 2020: 4 · 2018: 1
- FWCI
- 1.87
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Proceedings - International Conference on Image Processing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61872005, 61860206004, 61671018, 61702002)
- Anhui University (61860206004)
- Natural Science Foundation of Anhui Province (KJ2017A017, 1808085QF187)
- Institute of Physical Science and Information Technology, Anhui University
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- visual attention, dual attention mechanism, multimodal fusion, thermal infrared, global attention, local attention, visual object tracking
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