Profiling Pedestrian Distribution and Anomaly Detection in a Dynamic Environment
Profiling Pedestrian Distribution and Anomaly Detection in a Dynamic Environment
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动态环境中的行人分布分析和异常检测
DOI：
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发表时间：
2015
期刊：
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通讯作者：
C. Leckie
中科院分区：
文献类型：
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作者：
M. Doan;Sutharshan Rajasegarar;Mahsa Salehi;Masud Moshtaghi;C. Leckie
行人运动对城市的动态有着重要的影响，并为城市规划者提供有价值的指导。在本文中，我们模拟行人流量的正常行为和检测异常事件的行人计数数据的城市墨尔本。由于数据跨越延长的时段，并且行人活动可以间歇地改变（例如，活动在冬季与夏季），我们应用了一个包围切换模型，这是一个动态异常检测技术，可以适应系统之间切换不同的状态。结果进行了比较与静态聚类模型（HyCARCE），也交叉验证与已知的事件。我们发现，从包围开关模型的结果是有效的，比HyCARCE更准确。
Pedestrians movements have a major impact on the dynamics of cities and provide valuable guidance to city planners. In this paper we model the normal behaviours of pedestrian flows and detect anomalous events from pedestrian counting data of the City of Melbourne. Since the data spans an extended period, and pedestrian activities can change intermittently (e.g., activities in winter vs. summer), we applied an Ensemble Switching Model, which is a dynamic anomaly detection technique that can accommodate systems that switch between different states. The results are compared with those produced by a static clustering model (HyCARCE) and also cross-validated with known events. We found that the results from the Ensemble Switching Model are valid and more accurate than HyCARCE.