Video-based semantic analysis for on crowded rail stations

在这个项目中，我们提出使用先进的机器学习和人工智能来对火车站中的人群进行语义分析，通过大量非重叠摄像头进行监控。具体而言，我们将利用深度学习神经网络和跟踪算法来评估和监测火车站内的人群密度和动态。然后，我们提出了一个证据推理网络来提取先前数据分析的高级语义知识，以便有效地执行事件推理并过滤误报。该系统将向运营商提供与人群行为、遗弃物品、游荡和人群回避有关的预警警报。该项目建立在贝尔法斯特女王大学和BAE系统应用情报实验室现有的重要能力之上。

Video-based semantic analysis on crowded rail stationsIn this project, we propose the use of advance machine learning and artificial intelligence for the semantic analysis of crowds in train stations, monitored through a large set of non-overlapping cameras. Specifically, we will make use of deep learning neural networks and tracking algorithms for assessing and monitoring crowd density and dynamics within train stations. Then, we propose an evidential reasoning network to extract high-level semantic knowledge on the previous data analytics so event reasoning can be performed effectively and false positives can be filtered. This system will deliver early-warning alerts to operators relating to: crowd behaviour, abandoned objects, loitering and crowd avoidance. The project builds on significant existing capabilities at Queen's University Belfast and BAE Systems Applied Intelligence Laboratories.