人工智能
RGB颜色模型
BitTorrent跟踪器
计算机科学
计算机视觉
跟踪(教育)
融合
特征(语言学)
眼动
心理学
教育学
语言学
哲学
作者
Zhencheng Yu,Huijie Fan,Qiang Wang,Zhitian Li,Yandong Tang
出处
期刊:IEEE Signal Processing Letters
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:30: 1357-1361
标识
DOI:10.1109/lsp.2023.3316021
摘要
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.
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