Department of Video Information Processing, Korea University, Seoul 136-713, Korea.
Hanwha Systems Co., Sungnam 461-140, Korea.
Sensors (Basel). 2020 Jul 22;20(15):4081. doi: 10.3390/s20154081.
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.
视觉目标跟踪是监控系统的重要组成部分，已经开发出了许多高性能的方法。然而，这些跟踪方法往往针对红/绿/蓝（RGB）域进行优化，因此不适合用于红外（IR）域。为了克服这一缺点，许多研究人员已经构建了用于 IR 分析的数据集，包括那些为热红外视觉目标跟踪（VOT-TIR）挑战开发的数据集。因此，许多针对 IR 域的最先进的跟踪器已经被提出，但仍然需要用于防空监控系统的可靠的基于 IR 的跟踪器，包括为此目的构建一个新的 IR 数据集。在本文中，我们从光电监控系统中收集各种防空热波 IR（TIR）图像，以创建一个新的数据集。我们还提出了一个基于端到端卷积神经网络的框架，该框架用于学习防空目标（如无人机和无人机）的 IR 域中的目标跟踪。更具体地说，我们采用暹罗网络进行特征提取，并采用三个区域提议网络进行分类和回归分支。在推理阶段，所提出的网络被公式化为一种检测跟踪方法，并引入了用于模板分支的核滤波器，该核滤波器会为每一帧进行连续更新。所提出的网络能够在离线训练期间学习目标的稳健结构信息，并且核滤波器可以稳健地跟踪目标，从而提高了性能。来自新的 IR 数据集的实验结果表明，所提出的方法表现出色，实时处理速度为每秒 40 帧。
