  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.
