Abstract: To address the problems of insufficient training samples,significant cross-modal discrepancies,and inadequate coordination between spectral representation and spatial modeling in hyperspectral object tracking,a hyperspectral object tracking me-thod based on the dual-domain tuning and parameter-efficient transfer learning is proposed.Firstly,dual-domain harmony gate(DDHG) module is designed to perform dynamic interaction and adaptive fusion of RGB and hyperspectral features in the two-dimensional feature space,thereby enhancing the consistency and complementarity of cross-modal information.Then,flexible normalization adapter(FNA) is introduced to achieve efficient adaptation to the hyperspectral tracking task with only a small number of trainable parameters while keeping the pretrained backbone frozen,thus alleviating the overfitting and knowledge degradation caused by full fine-tuning under limited-sample conditions.Finally,experimental results on the HOTC20 and HOTC24 datasets show that the proposed method outperforms most competing methods in terms of tracking precision,success rate,and parameter efficiency.Specifically,it achieves a precision score of 0.930 and a success score of 0.693 on HOTC20,while also demonstrating strong robustness and competitiveness on the more challenging HOTC24 dataset.

Key words: Hyperspectral object tracking, Parameter-efficient transfer learning, Spectral-spatial feature, Gate fusion