Mining Abnormal Patterns in Moving Target Trajectories Based on Multi-Attribute Classification

As a type of time series data, trajectory data objectively record the location information and corresponding time information of an object’s activities. It not only describes the spatial activity trajectory of a moving object but also contains the unique attributes, states, and behavioral characteristics of the moving object itself. It can also reflect the interaction relationship between the object’s activities and various elements in the environment to a certain extent. Therefore, mining from moving target trajectory data to discover implicit, effective, and potentially useful spatiotemporal behavior patterns of moving targets, such as anomaly detection, will have significant research significance. This paper proposes a method for mining abnormal patterns in the trajectory of moving targets based on multi-attribute classification. Firstly, to explore the activity location patterns of single moving targets, a frequent sequence discovery method for moving targets based on sequence patterns is proposed. Furthermore, for moving target trajectory data sets containing multiple attributes, numerical attributes are extracted, and the data are clustered according to attribute classification to extract a set of normal behavior patterns of moving targets. Then, combining the activity location patterns and normal behavior patterns of the moving target, the original trajectory data are compared with them to achieve the goal of detecting abnormal behavior of the moving target. Finally, an incremental anomaly detection scheme is proposed to address the characteristics of fast updates and large numbers of data in trajectory data sets. This involves synchronously updating the frequency of moving target activity patterns and the range of values for normal behavior patterns while updating the trajectory data set, in order to meet the needs of database updates and improve the accuracy and credibility of results.