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.

中文翻译：

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通过利用波段选择网络的背景感知光谱信息进行鲁棒的高光谱物体跟踪


深色跟踪器主要使用预训练的卷积神经网络（CNN）进行分类和回归，但由于其有限的光谱信息，很难区分复杂背景中的目标。与彩色视频相比，高光谱视频由于丰富的基于材料的信息而提供了更好的判别能力。然而，很难为高光谱视频训练鲁棒的深度模型。关键问题是高光谱视频中存在大量冗余信息且训练样本不足。在这封信中，为高光谱跟踪任务设计了一种新的背景感知高光谱跟踪（BAHT）方法。我们的方法首先设计一个背景感知的波段选择模块，以保留可以更好地从背景中识别目标的波段。然后将选定的波段输入到主干网络，该主干网络在彩色视频上进行预训练，以描述具有深层语义特征的目标的外观。高光谱视频跟踪数据集上的实验表明，与流行的彩色和高光谱跟踪器相比，BAHT 跟踪器具有良好的性能。