Department of Computer Science, University of Reading, Reading RG6 6DH, UK.
AIT Austrian Institute of Technology, 1210 Vienna, Austria.
Sensors (Basel). 2022 Sep 28;22(19):7351. doi: 10.3390/s22197351.
Wide area surveillance has become of critical importance, particularly for border control between countries where vast forested land border areas are to be monitored. In this paper, we address the problem of the automatic detection of activity in forbidden areas, namely forested land border areas. In order to avoid false detections, often triggered in dense vegetation with single sensors such as radar, we present a multi sensor fusion and tracking system using passive infrared detectors in combination with automatic person detection from thermal and visual video camera images. The approach combines weighted maps with a rule engine that associates data from multiple weighted maps. The proposed approach is tested on real data collected by the EU FOLDOUT project in a location representative of a range of forested EU borders. The results show that the proposed approach can eliminate single sensor false detections and enhance accuracy by up to 50%.
广域监测变得至关重要，特别是对于那些需要监测大片森林接壤边境地区的国家之间的边境控制。在本文中，我们解决了在禁止活动区域（即森林接壤边境地区）自动检测活动的问题。为了避免在密集植被中使用单个传感器（如雷达）经常触发的误报，我们提出了一种使用被动红外探测器的多传感器融合和跟踪系统，结合了来自热和视觉摄像机图像的自动人员检测。该方法结合了加权图和一个规则引擎，该引擎将来自多个加权图的数据关联起来。所提出的方法在欧盟 FOLDOUT 项目在代表一系列欧盟森林接壤边境的地点收集的真实数据上进行了测试。结果表明，所提出的方法可以消除单传感器误报，并将准确率提高多达 50%。
