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Hyperspectral (HS) video is able to capture abundant spectral, spatial, and temporal information about objects, which overcomes the limitations of common red-green-blue (RGB) video in complex scenarios such as similar appearances and background clutters (BCs). However, most trackers apply hand-crafted features extracted from manually selected bands instead of deep features for object representations due to limited HS data and the band gap problem. Each HS image consists of many bands, and it is challenging to fully interact with the band information while maintaining tracking speed. To this end, this article proposes a novel end-to-end spectral awareness interaction network with a dynamic template (SPIRIT) for HS video object tracking. First, a spectral awareness module (SAM) is proposed to learn band contributions with consideration of nonlinear and global interactions between HS bands. It can also cooperate with the feature extraction module pretrained with RGB data to attenuate the band gap and data-hungry. Second, an interaction module (IM) is proposed to achieve inter and intraband feature interactions to enhance tracking performance while improving efficiency. Furthermore, the proposed method contains a novel update module (UM) that evaluates the tracking confidence of the current state to adapt to object changes and attenuate tracking drifts. Extensive experiments demonstrate the superiority of our approach compared to state-of-the-arts (SOTAs) while meeting real-time demands.
Chen et al. (Thu,) studied this question.