Low-Rank Regularized Correlation Filter for Hyperspectral Video Object Tracking - Na-Na Li; Heng-Chao Li; Jian-Li Wang; Xiong-Fei Geng; Jie Pan (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing)
Visual object tracking is crucial in both artificial intelligence and computer vision tasks. Hyperspectral videos can provide abundant spectral–spatial–temporal information, which brings new opportunities for improving object tracking performance in complex scenes. In this article, we propose a hyperspectral object tracking (HOT) algorithm using low-rank regularized correlation filter (LRCF), which fully leverages the relevance of object features. Specifically, to excavate deep semantic information of object regions, we first apply the band regrouping technology to divide the hyperspectral image into multiple three-channel false-color images and extract deep features via the pretrained network. Furthermore, a multifeature fusion strategy is designed, which integrates deep features with shallow handcrafted features (e.g., histograms of the gradient and color name features) through channel-wise concatenation, leveraging their complementary strengths to enhance the overall feature representation. Finally, we design the LRCF model by introducing low-rank constraint to classify and score search samples for object localization. Experimental results indicate that the LRCF-based tracking method is superior to existing HOT methods, achieving an area under curve score of 0.664 on the HOTC2020 dataset.