文献类型：期刊文献

英文题名：Siamese infrared and visible light fusion network for RGB-T tracking

作者：Peng, Jingchao[1]; Zhao, Haitao[1]; Hu, Zhengwei[1]; Zhuang, Yi[1]; Wang, Bofan[1]

机构：[1] East China University of Science and Technology, Automation Department, School of Information Science and Engineering, China

年份：2021

外文期刊名：arXiv

收录：EI(收录号：20210080745)

语种：英文

外文关键词：Benchmarking - Image fusion - Infrared imaging - Light - Tracking (position)

摘要：Due to the different photosensitive properties of infrared and visible light, the registered RGB-T image pairs shot in the same scene exhibit quite different characteristics. This paper proposes a siamese infrared and visible light fusion Network (SiamIVFN) for RBG-T image-based tracking. SiamIVFN contains two main subnetworks: a complementary-feature-fusion network (CFFN) and a contribution-aggregation network (CAN). CFFN utilizes a two-stream multilayer convolutional structure whose filters for each layer are partially coupled to fuse the features extracted from infrared images and visible light images. CFFN is a feature-level fusion network, which can cope with the misalignment of the RGB-T image pairs. Through adaptively calculating the contributions of infrared and visible light features obtained from CFFN, CAN makes the tracker robust under various light conditions. Experiments on two RGB-T tracking benchmark datasets demonstrate that the proposed SiamIVFN has achieved state-of-the-art performance. The tracking speed of SiamIVFN is 147.6FPS, the current fastest RGB-T fusion tracker. Copyright ? 2021, The Authors. All rights reserved.