人类轨迹数据中异常的动力学检测
Kinematic Detection of Anomalies in Human Trajectory Data
AI总结：
本文利用 Geolife 轨迹数据集研究人类移动的动力学特征，发现个体具有独特的“动力学特征画像”，并证明将简单动力学特征输入标准分类和异常检测算法可显著提升个体识别与异常检测效果。
摘要：
历史上，理解、建模和挖掘人类轨迹数据的许多研究都集中在个体停留的位置上。因此，现有研究的重点一直是用户去了哪里。另一方面，研究用户如何在位置之间移动具有巨大的新研究机会潜力。动力学特征描述了个体如何在位置之间移动，并可用于个体识别或异常检测等任务。不幸的是，数据可用性和质量方面的挑战使动力学轨迹挖掘变得困难。在本文中，我们利用人类轨迹的 Geolife 数据集来研究使用动力学特征识别个体和检测异常的可行性。我们表明，人类具有个体性的“动力学特征画像”，可以作为识别个体人类的强信号。我们通过实验表明，对于个体识别和异常检测这两个用例，将简单的动力学特征输入标准的分类和异常检测算法可显著改善结果。
英文摘要：
Historically, much of the research in understanding, modeling, and mining human trajectory data has focused on where an individual stays. Thus, the focus of existing research has been on where a user goes. On the other hand, the study of how a user moves between locations has great potential for new research opportunities. Kinematic features describe how an individual moves between locations and can be used for tasks such as identification of individuals or anomaly detection. Unfortunately, data availability and quality challenges make kinematic trajectory mining difficult. In this paper, we leverage the Geolife dataset of human trajectories to investigate the viability of using kinematic features to identify individuals and detect anomalies. We show that humans have an individual "kinematic profile" which can be used as a strong signal to identify individual humans. We experimentally show that, for the two use-cases of individual identification and anomaly detection, simple kinematic features fed to standard classification and anomaly detection algorithms significantly improve results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
