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作 者:张天路 张强 ZHANG Tianlu;ZHANG Qiang(School of Mechano-Electronic Engineering,Xidian University,Xi′an 710071)
机构地区:[1]西安电子科技大学机电工程学院,西安710071
出 处:《模式识别与人工智能》2023年第4期327-353,共27页Pattern Recognition and Artificial Intelligence
基 金:国家自然科学基金项目(No.61773301);陕西省创新团队项目(No.2018TD-012);卫星信息智能处理与应用技术重点实验室基金项目(No.2022-ZZKY-JJ-09-01)资助。
摘 要:RGB-热红外(RGB-Thermal,RGB-T)模态目标跟踪旨在利用RGB和热红外数据的互补性实现目标的稳健跟踪.目前基于深度学习的RGB-T目标跟踪前沿成果较多,但缺少系统且全面的综述性文献.因此,文中首先阐述RGB-T目标跟踪面临的挑战,分析总结目前主流的基于深度学习的RGB-T目标跟踪算法.具体来说,根据采用的基线(Baseline)方法不同,将已有方法划分为基于多域网络(Multi-domain Network,MDNet)的目标跟踪算法,基于孪生网络(Siamese Network)的目标跟踪算法和基于判别式相关滤波(Discriminative Correlation Filter,DCF)的目标跟踪算法.然后,介绍RGB-T目标跟踪任务中常用的数据集和评价指标,并在常用数据集上对比已有算法.最后,指出RGB-T目标跟踪领域未来可能的发展方向.RGB-Thermal(RGB-T)object tracking aims to achieve robust object tracking by utilizing the complementarity of RGB information and thermal infrared data.Currently,there are many cutting-edge achievements in RGB-T object tracking based on deep learning,but there is a lack of systematic and comprehensive review literature.In this paper,the challenges faced by RGB-T object tracking are elaborated,and the current mainstream RGB-T object tracking algorithms based on deep learning are analyzed and summarized.Specifically,the existing RGB-T trackers are divided into object tracking methods based on multi-domain network(MDNet),object tracking methods based on Siamese network and object tracking methods based on discriminative correlation filter(DCF)according to their different baselines.Then,the commonly used datasets and evaluation metrics in RGB-T object tracking tasks are introduced and the existing algorithms are compared on the commonly used datasets.Finally,the possible future development directions are pointed out.
关 键 词:目标跟踪 RGB-热红外(RGB-T) 深度学习 多域网络 孪生网络 判别式相关滤波
分 类 号:TP391[自动化与计算机技术—计算机应用技术]
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