Modality-correlation-aware sparse representation for RGB-infrared object tracking
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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 [197]
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- Authors
- Lan, X., Ye, M., Zhang, S., Zhou, H., & Yuen, P. C.
- Affiliations
- Hong Kong Baptist University (HK)
- Harbin Institute of Technology (CN)
- University of Leicester (GB)
- Corresponding author
- Ye, Mang
- Venue
- Pattern Recognition Letters
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 130
- Pages
- 12-20
- ISSN
- 0167-8655 (ISSN-L)
- OpenAlex
- W2896171544
- Cited by
- 121
- References
- 91
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 14 · 2023: 15 · 2022: 16 · 2021: 14 · 2020: 12 · 2019: 34 · 2018: 3
- FWCI
- 7.88
- Type
- journal article
- Language
- en
- Open access
- green
- figshare.com
- Licence
- Locations
- Pattern Recognition Letters (publishedVersion)
- Figshare (submittedVersion, cc-by-nc-nd)
- Leicester Research Archive (University of Leicester) (submittedVersion, cc-by-nc-nd)
- Funders
- Engineering and Physical Sciences Research Council (EP/R007187/1, EP/N508664/1, EP/N011074/1)
- 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 › Engineering › Aerospace Engineering › Infrared Target Detection Methodologies
- Keywords
- sparse representation, multimodal fusion, dual-camera system, infrared video, feature representation, illumination variation, low-light tracking
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