Hubner Michael, Wohlleben Kilian, Litzenberger Martin, Veigl Stephan, Opitz Andreas, Grebien Stefan, Graf Franz, Haderer Andreas, Rechbauer Susanne, Poltschak Sebastian
AIT Austrian Institute of Technology, 1210 Vienna, Austria.
Joanneum Research Forschungsgeselllschaft mbH, 8010 Graz, Austria.
Sensors (Basel). 2024 Jun 25;24(13):4118. doi: 10.3390/s24134118.
Effective security surveillance is crucial in the railway sector to prevent security incidents, including vandalism, trespassing, and sabotage. This paper discusses the challenges of maintaining seamless surveillance over extensive railway infrastructure, considering both technological advances and the growing risks posed by terrorist attacks. Based on previous research, this paper discusses the limitations of current surveillance methods, particularly in managing information overload and false alarms that result from integrating multiple sensor technologies. To address these issues, we propose a new fusion model that utilises Probabilistic Occupancy Maps (POMs) and Bayesian fusion techniques. The fusion model is evaluated on a comprehensive dataset comprising three use cases with a total of eight real life critical scenarios. We show that, with this model, the detection accuracy can be increased while simultaneously reducing the false alarms in railway security surveillance systems. This way, our approach aims to enhance situational awareness and reduce false alarms, thereby improving the effectiveness of railway security measures.
有效的安全监控对于铁路部门预防安全事件至关重要，这些事件包括故意破坏、非法侵入和蓄意破坏。本文讨论了在广泛的铁路基础设施上保持无缝监控所面临的挑战，同时考虑了技术进步以及恐怖袭击带来的日益增长的风险。基于先前的研究，本文讨论了当前监控方法的局限性，特别是在管理因集成多种传感器技术而导致的信息过载和误报方面。为了解决这些问题，我们提出了一种新的融合模型，该模型利用概率占用地图（POM）和贝叶斯融合技术。该融合模型在一个包含三个用例、共八个现实生活关键场景的综合数据集上进行了评估。我们表明，通过该模型，可以提高检测准确率，同时减少铁路安全监控系统中的误报。通过这种方式，我们的方法旨在增强态势感知并减少误报，从而提高铁路安全措施的有效性。
