Low-quality modalities contain not only a lot of noisy information but also some discriminative features in RGBT tracking. However, the potentials of low-quality modalities are not well explored in existing RGBT tracking algorithms. In this work, we propose a novel duality-gated mutual condition network to fully exploit the discriminative information of all modalities while suppressing the effects of data noise. In specific, we design a mutual condition module, which takes the discriminative information of a modality as the condition to guide feature learning of target appearance in another modality. Such module can effectively enhance target representations of all modalities even in the presence of low-quality modalities. To improve the quality of conditions and further reduce data noise, we propose a duality-gated mechanism and integrate it into the mutual condition module. To deal with the tracking failure caused by sudden camera motion, which often occurs in RGBT tracking, we design a resampling strategy based on optical flow algorithms. It does not increase much computational cost since we perform optical flow calculation only when the model prediction is unreliable and then execute resampling when the sudden camera motion is detected. Extensive experiments on four RGBT tracking benchmark datasets show that our method performs favorably against the state-of-the-art tracking algorithms
翻译:低质量模式不仅包含大量的噪音信息,而且还包含RGBT跟踪中的一些歧视性特征。然而,低质量模式的潜力在现有的RGBT跟踪算法中没有得到很好的探讨。在这项工作中,我们提议建立一个新型的双重性组合共条件网络,充分利用所有模式的歧视性信息,同时抑制数据噪音的影响。具体地说,我们设计了一个共同条件模块,将某种模式的歧视性信息作为在另一种模式中显示目标外观特征的导导导条件。这种模块可以有效地加强所有模式的目标表达方式,即使存在低质量模式。为了提高条件质量和进一步减少数据噪音,我们提议了一个双重性调整机制,并将其纳入共同条件模块。为了处理由于突然的相机动作造成的故障,通常发生在RGBT跟踪中。我们设计了一个基于光学流算法的重新标注战略。它不会增加很多计算成本,因为我们只有在模型预测不可靠的情况下才进行光流计算,然后在检测到突然的相机动作时再进行抽查。关于四种RGBT偏向基准跟踪方法的大规模实验,对四种RGBT跟踪基准数据显示的状态进行。