Kinematic Detection of Anomalies in Human Trajectory Data

TL;DR

This paper explores the use of kinematic features derived from human movement data to identify individuals and detect anomalies, demonstrating that simple features can significantly enhance classification and detection performance.

Contribution

It introduces the concept of using kinematic profiles for human identification and anomaly detection, leveraging the Geolife dataset to validate the approach.

Findings

Kinematic profiles serve as strong individual identifiers.

Simple features improve classification accuracy.

Effective anomaly detection using kinematic data.

Abstract

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.