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Weighted Kernel Filter Based Anti-Air Object Tracking for Thermal Infrared Systems

机译：基于加权核滤波器的热红外系统的防空对象跟踪

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Visual object tracking is an important component of surveillance systems and many high-performance methods have been developed. However, these tracking methods tend to be optimized for the Red/Green/Blue (RGB) domain and are thus not suitable for use with the infrared (IR) domain. To overcome this disadvantage, many researchers have constructed datasets for IR analysis, including those developed for The Thermal Infrared Visual Object Tracking (VOT-TIR) challenges. As a consequence, many state-of-the-art trackers for the IR domain have been proposed, but there remains a need for reliable IR-based trackers for anti-air surveillance systems, including the construction of a new IR dataset for this purpose. In this paper, we collect various anti-air thermal-wave IR (TIR) images from an electro-optical surveillance system to create a new dataset. We also present a framework based on an end-to-end convolutional neural network that learns object tracking in the IR domain for anti-air targets such as unmanned aerial vehicles (UAVs) and drones. More specifically, we adopt a Siamese network for feature extraction and three region proposal networks for the classification and regression branches. In the inference phase, the proposed network is formulated as a detection-by-tracking method, and kernel filters for the template branch that are continuously updated for every frame are introduced. The proposed network is able to learn robust structural information for the targets during offline training, and the kernel filters can robustly track the targets, demonstrating enhanced performance. Experimental results from the new IR dataset reveal that the proposed method achieves outstanding performance, with a real-time processing speed of 40 frames per second.
机译：视觉对象跟踪是监控系统的重要组成部分，并且已经开发了许多高性能方法。然而，这些跟踪方法倾向于针对红色/绿色/蓝色（RGB）域进行优化，因此不适用于红外（IR）域。为了克服这个缺点，许多研究人员已经构建了IR分析的数据集，包括为热红外视觉对象跟踪（VOT-TIR）挑战开发的数据集。因此，已经提出了许多用于IR域的最先进的追踪器，但仍然需要用于防空系统的可靠的基于IR的跟踪器，包括为此目的构造新的IR数据集。在本文中，我们从电光监控系统中收集各种抗空气热波IR（TIR）图像以创建新数据集。我们还基于端到端卷积神经网络的框架介绍了一种学习IR域中的对象跟踪，用于防空目标，例如无人驾驶飞行器（无人机）和无人机。更具体地，我们采用暹罗网络用于分类和回归分支的特征提取和三个区域提案网络。在推理阶段，所提出的网络被制定为逐步检测方法，并且介绍了用于每个帧的连续更新的模板分支的内核过滤器。所提出的网络能够在离线训练期间为目标学习强大的结构信息，并且内核过滤器可以强大地跟踪目标，展示增强的性能。新IR数据集的实验结果表明，该方法实现了出色的性能，实时处理速度为每秒40帧。

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