HPFormer: Hyperspectral image prompt object tracking

Hyperspectral imagery contains abundant spectral information beyond the visible RGB bands, providing rich discriminative details about objects in a scene. Leveraging such data has the potential to enhance visual tracking performance. While prior hyperspectral trackers employ CNN or hybrid CNN-Transformer architectures, we propose a novel approach HPFormer on Transformers to capitalize on their powerful representation learning capabilities. The core of HPFormer is a Hyperspectral Hybrid Attention (HHA) module which unifies feature extraction and fusion within one component through token interactions. Additionally, a Transform Band Module (TBM) is introduced to selectively aggregate spatial details and spectral signatures from the full hyperspectral input for injecting informative target representations. Extensive experiments demonstrate state-of-the-art performance of HPFormer on benchmark NIR and VIS tracking datasets. Our work provides new insights into harnessing the strengths of transformers and hyperspectral fusion to advance robust object tracking.