论文标题
RGBT跟踪的二元门控相互状态网络
Duality-Gated Mutual Condition Network for RGBT Tracking
论文作者
论文摘要
低质量的模式不仅包含许多嘈杂的信息，还包含RGBT跟踪中的一些歧视性特征。但是，在现有的RGBT跟踪算法中，低质量方式的潜力尚未得到很好的探索。在这项工作中，我们提出了一个新型的二元门控相互条件网络，以完全利用所有方式的歧视性信息，同时抑制数据噪声的影响。在具体而言，我们设计了一个共同条件模块，该模块将模态的歧视性信息作为指导目标外观的特征学习在另一种方式中的条件。即使在存在低质量方式的情况下，这种模块也可以有效地增强所有模式的目标表示。为了提高条件的质量并进一步降低数据噪声，我们提出了偶性门控机制，并将其整合到互惠状态模块中。为了处理通常在RGBT跟踪中发生的突然相机运动引起的跟踪失败，我们设计了基于光流算法的重采样策略。它不会增加太多的计算成本，因为我们仅在模型预测不可靠时执行光流计算，然后在检测到突然的摄像头运动时执行重新采样。四个RGBT跟踪基准数据集的广泛实验表明，我们的方法对最新跟踪算法的性能有利
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
