Robust Hyperspectral Object Tracking by Exploiting Background-Aware Spectral Information With Band Selection Network
Yiming Tang, Yufei Liu, Ling Ji, Hong Huang
Abstract
Deep color trackers mainly use pre-trained convolutional neural networks for classification and regression, but it is difficult to discriminate targets in complex backgrounds for its limited spectral information. Compared with color video, hyperspectral videos provide better discriminative ability due to the abundant material-based information. However, it is hard to train a robust deep model for hyperspectral videos. The key issues are that there exists much redundant information in hyperspectral videos and the training samples are inadequate. In this paper, a new background aware hyperspectral tracking (BAHT) method is designed for hyperspectral tracking task. Our method firstly designs a background aware band selection module to preserve bands that can better recognize a target from backgrounds. Then the selected bands are input to backbone networks, which are pre-trained on color videos, to describe the appearances of targets with deep semantic features. Experiments on hyperspectral video tracking dataset illustrate the good performance of BAHT tracker compared with popular color and hyperspectral trackers.