CBFF-Net: A New Framework for Efficient and Accurate Hyperspectral Object Tracking
Long Gao, Pan Liu, Yan Jiang, Weiying Xie, Jie Lei, Yunsong Li, Qian Du
Abstract
Visual object tracking is a fundamental task in computer vision, and thrived in recent decades. With the development of snapshot hyperspectral sensors, efforts have been made to exploit tracking the object with hyperspectral (HS) videos to overcome the inherent limitation of RGB images. Existing HS tracking algorithms extract the deep features from image data separately, which break the interaction information between bands. Therefore, the discrimination ability of HS trackers is limited and the efficiency of the existing HS algorithms is low. In this paper, a novel algorithm (CBFF-Net) is proposed for HS object tracking to improve the discrimination ability and reduce the computational complexity. Specifically, the backbone and head network are implemented with modules of a transferred RGB object tracking network to carry out the HS target tracking task while maintaining the discrimination ability learned from RGB data. Moreover, a bi-directional multiple deep feature fusion (BMDFF) module is proposed to fuse the features extracted from different bands of the HS images, and a cross-band group attention (CBGA) module is introduced to learn interaction information across bands of the HS images. Experiments results indicate the superiority in performance of CBFF-Net, and it runs at 24 frames per second.