人的运动模式有独特性，可用来识别身份和发现异常行为。
Kinematic Detection of Anomalies in Human Trajectory Data
- 通过分析运动轨迹中的速度、加速度等特征，构建个体运动画像。
- 在真实数据上验证，简单特征使识别与异常检测准确率显著提升。
- 适合对轨迹安全、智能监控感兴趣的开发者与研究人员。
以往的人类轨迹研究多聚焦于用户停留的位置，而对用户移动方式的研究潜力尚未充分挖掘。运动学特征描述了个体在位置间移动的方式，可用于身份识别或异常检测。然而，数据可用性和质量限制了运动学轨迹挖掘的发展。本文利用人类轨迹数据集Geolife，探究使用运动学特征进行个体识别和异常检测的可行性。实验表明，每个人具有独特的“运动学特征画像”，可作为识别个体的强信号。对于身份识别与异常检测两个任务，将简单运动学特征输入标准分类与异常检测算法，能显著提升性能。
原文摘要 · Abstract (English)
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.
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