In this study, we propose a novel RGB-T tracking framework by jointly modeling both appearance and motion cues. First, to obtain a robust appearance model, we develop a novel late fusion method to infer the fusion weight maps of both RGB and thermal (T) modalities. The fusion weights are determined by using offline-trained global and local multimodal fusion networks, and then adopted to linearly combine the response maps of RGB and T modalities. Second, when the appearance cue is unreliable, we comprehensively take motion cues, i.e., target and camera motions, into account to make the tracker robust. We further propose a tracker switcher to switch the appearance and motion trackers flexibly. Numerous results on three recent RGB-T tracking datasets show that the proposed tracker performs significantly better than other state-of-the-art algorithms.

中文翻译：

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联合建模运动和外观提示以实现可靠的RGB-T跟踪

在这项研究中，我们通过对外观和运动提示进行联合建模，提出了一种新颖的RGB-T跟踪框架。首先，为了获得鲁棒的外观模型，我们开发了一种新颖的后期融合方法来推断RGB和热（T）模态的融合权重图。融合权重是通过使用离线训练的全局和局部多模态融合网络确定的，然后用于线性组合RGB和T模态的响应图。其次，当外观提示不可靠时，我们会综合考虑运动提示（即目标和摄像机的运动），以使跟踪器更健壮。我们还提出了一种跟踪器切换器，可以灵活地切换外观和运动跟踪器。在三个最新的RGB-T跟踪数据集中获得的大量结果表明，所提出的跟踪器的性能明显优于其他最新算法。