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Robust Hyperspectral Object Tracking by Exploiting Background-Aware Spectral Information With Band Selection Network
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AI
DOI：10.1109/LGRS.2022.3202039.png)
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Abstract
En 中文
Deep color trackers mainly use pretrained convolutional neural networks (CNNs) 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 letter, a new background-aware hyperspectral tracking (BAHT) method is designed for hyperspectral tracking task. Our method first 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 the backbone networks, which are pretrained on color videos, to describe the appearances of targets with deep semantic features. Experiments on the hyperspectral video tracking dataset illustrate the good performance of BAHT tracker compared with popular color and hyperspectral trackers.
Keywords:
Hyperspectral sensors
Feature extraction
Target tracking
Training
Color
Semantics
Object tracking
Band selection
deep learning
hyperspectral videos
Siamese network
visual tracking
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