Region Selective Fusion Network for Robust RGB-T Tracking
RGB-T tracking utilizes thermal infrared images as a complement to visible light images in order to perform more robust visual tracking in various scenarios. However, the highly aligned RGB-T image pairs introduces redundant information, the modal quality fluctuation during tracking also brings unre...
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Published in | IEEE signal processing letters Vol. 30; pp. 1357 - 1361 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
New York
IEEE
2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
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Summary: | RGB-T tracking utilizes thermal infrared images as a complement to visible light images in order to perform more robust visual tracking in various scenarios. However, the highly aligned RGB-T image pairs introduces redundant information, the modal quality fluctuation during tracking also brings unreliable information. Existing RGB-T trackers usually use channel-wise multi-modal feature fusion in which the low-quality features degrades the fused features and causes trackers to drift. In this work, we propose a region selective fusion network that first evaluates each image region by cross-modal and cross-region modeling, then removes low-quality redundant region features to alleviate the negative effects caused by unreliable information in multi-modal fusion. Besides, the region removal scheme brings a efficiency boost as redundant features are removed progressively, this enables the tracker to run at a high tracking speed. Extensive experiments show that the proposed tracker achieves competitive performance with a real-time tracking speed on multiple RGB-T tracking benchmarks including LasHeR, RGBT234 and GTOT. |
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ISSN: | 1070-9908 1558-2361 |
DOI: | 10.1109/LSP.2023.3316021 |