RGB-Thermal object tracking attempt to locate target object using
complementary visual and thermal infrared data. Existing RGB-T trackers fuse
different modalities by robust feature representation learning or adaptive
modal weighting. However, how to integrate dual attention mechanism for visual
tracking is still a subject that has not been studied yet. In this paper, we
propose two visual attention mechanisms for robust RGB-T object tracking.
Specifically, the local attention is implemented by exploiting the common
visual attention of RGB and thermal data to train deep classifiers. We also
introduce the global attention, which is a multi-modal target-driven attention
estimation network. It can provide global proposals for the classifier together
with local proposals extracted from previous tracking result. Extensive
experiments on two RGB-T benchmark datasets validated the effectiveness of our
proposed algorithm.

本文提出了两种用于 RGB-T 目标跟踪的视觉关注机制，其中包括利用 RGB 和热感数据的局部关注和多模态目标驱动关注估计网络的全局关注。在两个 RGB-T 基准数据集上的实验验证了我们提出算法的有效性。

学习面向目标的双重注意力，实现 RGB-T 跟踪的稳健性

Learning Target-oriented Dual Attention for Robust RGB-T Tracking

This paper investigates how to perform robust visual tracking in adverse and
challenging conditions using complementary visual and thermal infrared data
(RGBT tracking). We propose a novel deep network architecture called
qualityaware Feature Aggregation Network (FANet) for robust RGBT tracking.
Unlike existing RGBT trackers, our FANet aggregates hierarchical deep features
within each modality to handle the challenge of significant appearance changes
caused by deformation, low illumination, background clutter and occlusion. In
particular, we employ the operations of max pooling to transform these
hierarchical and multi-resolution features into uniform space with the same
resolution, and use 1x1 convolution operation to compress feature dimensions to
achieve more effective hierarchical feature aggregation. To model the
interactions between RGB and thermal modalities, we elaborately design an
adaptive aggregation subnetwork to integrate features from different modalities
based on their reliabilities and thus are able to alleviate noise effects
introduced by low-quality sources. The whole FANet is trained in an end-to-end
manner. Extensive experiments on large-scale benchmark datasets demonstrate the
high-accurate performance against other state-of-the-art RGBT tracking methods.

本研究提出了一种名为质量感知特征聚合网络（FANet）的神经网络架构，用于在恶劣和具有挑战性的条件下使用补充的视觉和热红外数据进行强健的可见光跟踪，并对其进行了广泛的基准数据集实验证明了其与其他最先进的 RGBT 跟踪方法相比较高的准确性表现。