Hyperspectral imagery provides abundant spectral information beyond the visible RGB bands, offering rich discriminative details about objects in a scene. Leveraging such data has the potential to enhance visual tracking performance. In this paper, we propose a hyperspectral object tracker based on hybrid attention (HHTrack). The core of HHTrack is a hyperspectral hybrid attention (HHA) module that unifies feature extraction and fusion within one component through token interactions. A hyperspectral bands fusion (HBF) module is also introduced to selectively aggregate spatial and spectral signatures from the full hyperspectral input. Extensive experiments demonstrate the state-of-the-art performance of HHTrack on benchmark Near Infrared (NIR), Red Near Infrared (Red-NIR), and Visible (VIS) hyperspectral tracking datasets. Our work provides new insights into harnessing the strengths of transformers and hyperspectral fusion to advance robust object tracking.

翻译：高光谱成像提供了超越可见光RGB波段的丰富光谱信息，为场景中的目标物体呈现了更为精细的判别细节。利用这类数据有望提升视觉跟踪性能。本文提出一种基于混合注意力机制的高光谱目标跟踪器（HHTrack）。其核心是面向高光谱的混合注意力（HHA）模块，该模块通过令牌交互统一了特征提取与融合功能。同时引入高光谱波段融合（HBF）模块，从全量高光谱输入中选择性聚合空间与光谱特征。大量实验表明，HHTrack在基准近红外（NIR）、红-近红外（Red-NIR）和可见光（VIS）高光谱跟踪数据集上均达到了最先进性能。本研究为利用Transformer与高光谱融合的优势推动鲁棒目标跟踪提供了新思路。