论文标题
共同建模运动和外观提示，用于强大的RGB-T跟踪
Jointly Modeling Motion and Appearance Cues for Robust RGB-T Tracking
论文作者
论文摘要
在这项研究中，我们通过共同建模外观和运动提示来提出一个新型的RGB-T跟踪框架。首先，为了获得强大的外观模型，我们开发了一种新型的晚期融合方法来推断RGB和热（T）模态的融合权重图。融合权重通过使用离线训练的全局和局部多模式融合网络确定，然后采用以线性结合RGB和T模式的响应图。其次，当外观提示不可靠时，我们全面地考虑了运动提示，即目标和摄像机运动，以使跟踪器强大。我们进一步提出了一个跟踪器开关，以灵活地切换外观和运动跟踪器。最近三个RGB-T跟踪数据集的许多结果表明，所提出的跟踪器的性能明显优于其他最先进的算法。
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.
