ProFiT: A prompt-guided frequency-aware filtering and template-enhanced interaction framework for hyperspectral video tracking
Yuzeng Chen, Qiangqiang Yuan, Yuqi Tang, Xin Wang, Yi Xiao, Jiang He, Ziyang Lihe, Xianyu Jin
Abstract
Hyperspectral (HSP) video data can offer rich spectral-spatial–temporal information crucial for capturing object dynamics, attenuating the drawbacks of classical unimodal and multi-modal tracking. Current HSP tracking arts often suffer from feature refinements and information interactions, sealing the ceiling of capabilities. This study presents ProFiT, an innovative prompt-guided frequency-aware filtering and template-enhanced interaction framework for HSP video tracking, mitigating the above issues. First, ProFiT introduces a frequency-aware filtering module with adaptive filter generators to refine spectral-spatial consistency within HSP and false-color features. Then, a template-enhanced interaction module is introduced to extract complementary information for efficient cross-modal interactions. Furthermore, a token fusion module is devised to capture contextual dependencies with minimal parameters. While a temporal decoder embeds historical states, guiding to ensure temporal coherence. Comprehensive experiments across nine HSP benchmarks demonstrate that ProFiT achieves competitive accuracy, with overall precision and success rate scores of 0.870 and 0.678, respectively, along with a frame per second of 39.5. These results outperform 59 state-of-the-art trackers, establishing ProFiT as a robust solution for HSP video tracking. The code and result will be accessible at: https://github.com/YZCU/ProFiT .