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- W4386766604 abstract "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 channelwise 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." @default.
- W4386766604 created "2023-09-16" @default.
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- W4386766604 date "2023-01-01" @default.
- W4386766604 modified "2023-10-13" @default.
- W4386766604 title "Region Selective Fusion Network for Robust RGB-T Tracking" @default.
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- W4386766604 doi "https://doi.org/10.1109/lsp.2023.3316021" @default.
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