DSP-Net: A Dynamic Spectral–Spatial Joint Perception Network for Hyperspectral Target Tracking

In order to effectively utilize spectral and object spatial information to improve tracking performance, we design an Hyperspectral Video (HSV) tracker, namely DSP-Net, to integrate the various prior information. The gradient difference between spectral vectors is explored to develop a clustering technique. The approach generates a binary mask containing target spectral information and appearance clues. A feature cache is introduced to store historical information. Additionally, the channel shift operation is used on the timing to capture the trajectory clues of the target. With the help of the non-local mechanism, the trajectory clues, appearance clues and spectral information of the target are finally integrated, using the designed spectral-spatial joint perception module to enhance the expression of the target. Experimental results show that DSP-Net outperforms state-of-the-art HSV trackers on existing dataset.