HHTrack: Hyperspectral Object Tracking Using Hybrid Attention
This work addresses robust object tracking for applications like surveillance or remote sensing by leveraging hyperspectral data, but it appears incremental as it builds on existing transformer and fusion techniques.
The paper tackled hyperspectral object tracking by proposing HHTrack, a tracker using hybrid attention and hyperspectral bands fusion, achieving state-of-the-art performance on benchmark datasets including NIR, Red-NIR, and VIS.
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.