Historical Object-Aware Prompt Learning for Universal Hyperspectral Object Tracking
Lu Zhang, Rui Yao, Yuhong Zhang, Yong Zheng Zhou, Fuyuan Hu, Jiaqi Zhao, Zhiwen Shao · ACM Transactions on Multimedia Computing Communications and Applications · 2025
Hyperspectral Object Tracking (HOT), utilizing rich spectral information from hyperspectral video (HSV), holds significant importance for object tracking. We identify that a major obstacle in improving HOT performance lies in effectively leveraging spectral and historical information. Furthermore, due to the mismatch in band dimensions between hyperspectral and RGB images, state-of-the-art RGB-based trackers struggle to adapt to unified HOT tasks. To address this, we propose a Historical Object-Aware Prompt Learning (HOPL) method for universal hyperspectral object tracking. Initially, we transform hyperspectral image ( \( N \) bands) into multiple sets of three bands with different combinations and feed them into a backbone network to generate base features. Subsequently, we introduce a historical object-aware prompter, where historical object-aware images are input to generate prompt features that enhance the representation of object information when combined with base features. Additionally, we design a band information fusion module to integrate the multiple sets of base features. By introducing historical object-aware prompts, HOPL significantly enhances tracking performance without retraining the backbone network. Experimental results on the HOT2023 dataset (comprising HSV with 25-band, 16-band, and 15-band wavelength ranges) and HOT2022 dataset validate the superiority of HOPL over state-of-the-art methods. The source code is available at https://github.com/rayyao/HOPL .