Abstract Due to the low signal-to-noise ratios and fast motion of aerial targets, tracking aircraft based on infrared sequences is still a challenging task. To develop a robust and fast aerial infrared target tracking method, a new framework based on a Siamese network is proposed. Traditional features, namely, the aspect ratio and mean contrast, are used to improve the performance of the tracker. We performed experiments on three simulated infrared sequences to compare the proposed method with nine state-of-the-art tracking algorithms. To make the experimental results more credible, we also performed experiments on six real shot sequences that were not used when training the network. These experiments clearly demonstrate that our algorithm can reliably track an aerial infrared target while running at a real-time speed. The accuracy of our approach is improved by 136.3% compared with CFNet.

中文翻译：

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基于Siamese网络和传统特征的航空红外目标跟踪

摘要 由于空中目标信噪比低、运动速度快，基于红外序列的飞行器跟踪仍然是一项具有挑战性的任务。为了开发一种鲁棒且快速的空中红外目标跟踪方法，提出了一种基于 Siamese 网络的新框架。传统特征，即纵横比和平均对比度，用于提高跟踪器的性能。我们对三个模拟红外序列进行了实验，以将所提出的方法与九种最先进的跟踪算法进行比较。为了使实验结果更加可信，我们还对训练网络时未使用的六个实拍序列进行了实验。这些实验清楚地表明，我们的算法可以在以实时速度运行的同时可靠地跟踪空中红外目标。