Statistical Anomaly Detection in Human Dynamics Monitoring Using a Hierarchical Dirichlet Process Hidden Markov Model

Understanding of human dynamics has drawn attention to various areas. The wide spread of positioning technologies, such as GPS facilitates location information to be obtained with high spatio-temporal resolution as well as at low costs. By collecting individual location information in real time, monitoring of human dynamics has recently become possible and is expected to the area of dynamic traffic control. In this monitoring, detecting anomalous states of human dynamics become important. This research aims to define an anomaly detection problem of the human dynamics monitoring with time-series gridded population data and develop an anomaly detection method for this problem. According to the result of a review we have conducted, we discussed the characteristics of the anomaly detection in human dynamics monitoring and categorized our problem to a semi-supervised anomaly detection problem that detects contextual anomalies behind time-series data. We developed an anomaly detection method based on a sticky hierarchical Dirichlet process hidden Markov model, which is able to estimate the number of latent states according to the input data. Results of the experiment with synthetic data showed that our proposed method has good fundamental performance with respect to the detection rate. Through the experiments with real gridded population data, anomalies were detected when and where an actual social event had occurred.