A Recursive Bayesian Filter for Anomalous Behavior Detection in Trajectory Data

Abstract

This chapter presents an original approach to anomalous behavior analysis in trajectory data by means of a recursive Bayesian filter. The anomalous pattern detection is of great interest in the areas of navigation, driver assistant system, surveillance and emergency management. In this work we focus on the GPS trajectories finding where the driver is encountering navigation problems, i.e., taking a wrong turn, performing a detour or tending to lose his way. To extract the related features, i.e., turns and their density, degree of detour and route repetition, a long-term perspective is required to observe data sequences instead of individual data points. We therefore employ high-order Markov chain to remodel the trajectory integrating these long-term features. A recursive Bayesian filter is conducted to process the Markov model and deliver an optimal probability distribution of the potential anomalous driving behaviors dynamically over time. The proposed filter performs unsupervised detection in single trajectory with solely the local features. No training process is required to characterize the anomalous behaviors. Based on the results of individual trajectories collective behaviors can be analyzed as well to indicate some traffic issues, e.g., turn restriction, blind alley, temporary road-block, etc. Experiments are performed on the trajectory data in urban areas demonstrating the potential of this approach.