Hyperspectral Tracker with Constrained Object Adaptive Learning and Trajectory Construction

Hyperspectral imaging offers significant potential for precise object tracking, yet the scarcity of dataset volumes specifically tailored for hyperspectral tracking algorithms hinders progress, particularly for deep models with complex structures. Additionally, current deep learning-based hyperspectral trackers typically enhance model accuracy via online or adversarial learning, adversely affecting tracking speed. To address these challenges, this paper introduces the Constrained Object Adaptive Learning hyperspectral Tracker (COALT), an effective parameter-efficient fine-tuning tracker tailored for hyperspectral tracking. COALT integrates Pixel-level Object Constrained Spectral Prompt (POCSP) and Temporal Sequence Trajectory Prompt (TSTP) through Adaptive Learning with Parameter-efficient Fine-tuning (ALPEFT), enabling a transformer-based tracker to capture detailed spectral features and relationships in hyperspectral image sequences through trainable rank decomposition matrices. Specifically, POCSP is designed to retain optimal spectral information with low internal correlation and high object representativeness, enabling rapid image reconstruction. Then, the most representative spectral template and search are fused into a single stream as spectral prompts for the Encoder and Decoder layers. Concurrently, the previous coordinates within the same sequence are tokenized and utilized as temporal prompts by TSTP in the decoder layers. The model is trained with ALPEFT to optimize spectral information learning, which substantially reduces the number of training parameters, alleviating overfitting issues arising from limited data. Meanwhile, the proposed tracker not only retains the ability of pre-trained model to estimate object trajectories in an autoregressive manner but also effectively utilizes spectral information and enhances target location perception during the fine-tuning process. Extensive experiments and evaluations are conducted on two public hyperspectral tracking datasets. The results demonstrate that the proposed COALT tracker achieves satisfactory performance with leading processing speed. The code will be available at https://github.com/PING-CHUANG/COALT.