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Real-time movement detection and analysis for video surveillance applications

机译：视频监控应用的实时运动检测和分析

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Pedestrian movement along critical infrastructures like pipes, railways or highways, is of major interest in surveillance applications as well as its behavior in urban environment. The goal is to anticipate illicit or dangerous human activities. For this purpose, we propose an all-in-one small autonomous system which delivers high level statistics and reports alerts in specific cases. This situational awareness project leads us to manage efficiently the scene by performing movement analysis. A dynamic background extraction algorithm is developed to reach the degree of robustness against natural and urban environment perturbations and also to match the embedded implementation constraints. When changes are detected in the scene, specific patterns are applied to detect and highlight relevant movements. Depending on the applications, specific descriptors can be extracted and fused in order to reach a high level of interpretation. In this paper, our approach is applied to two operational use cases: pedestrian urban statistics and railway surveillance. In the first case, a grid of prototypes is deployed over a city centre to collect pedestrian movement statistics up to a macroscopic level of analysis. The results demonstrate the relevance of the delivered information; in particular, the flow density map highlights pedestrian preferential paths along the streets. In the second case, one prototype is set next to high speed train tracks to secure the area. The results exhibit a low false alarm rate and assess our approach of a large sensor network for delivering a precise operational picture without overwhelming a supervisor.
机译：沿关键基础设施（如管道，铁路或高速公路）的行人移动在监视应用及其在城市环境中的行为方面引起了人们的极大兴趣。目的是预测非法或危险的人类活动。为此，我们提出了一个多合一的小型自治系统，该系统可以提供高级别的统计信息并在特定情况下报告警报。这个态势感知项目使我们能够通过执行运动分析来有效地管理场景。开发了一种动态背景提取算法，以达到抵御自然和城市环境扰动的鲁棒程度，并与嵌入式实现约束相匹配。在场景中检测到变化时，将应用特定的模式来检测并突出显示相关的移动。取决于应用，可以提取并融合特定的描述符，以达到较高的解释水平。在本文中，我们的方法适用于两个操作用例：行人城市统计和铁路监视。在第一种情况下，将原型网格部署在市中心上方，以收集行人运动统计数据，直至进行宏观分析。结果表明所提供信息的相关性；特别是，流量密度图突出显示了沿街道的行人优先路径。在第二种情况下，将一个原型放置在高速火车轨道旁，以确保该区域的安全。结果显示出较低的误报率，并评估了我们使用大型传感器网络来提供精确的操作画面而不会引起主管人员压倒的方法。

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