Abstract
Using personal trajectory information to grasp the spatiotemporal laws of dangerous activities to curb the occurrence of criminal acts is a new opportunity and method for security prevention and control. This paper proposes a novel method to discover abnormal behaviors and judge abnormal behavior patterns using mobility trajectory data. Abnormal behavior trajectory refers to the behavior trajectory whose temporal and spatial characteristics are different from normal behavior, and it is an important clue to discover dangerous behavior. Abnormal patterns are the behavior patterns summarized based on the regular characteristics of criminals’ activities, including wandering, scouting, random walk, and trailing. This paper examines the abnormal behavior patterns based on mobility trajectories. A Long Short-Term Memory Network (LSTM)-based method is used to extract personal trajectory features, and the K-means clustering method is applied to extract abnormal trajectories from the trajectory dataset. Based on the characteristics of different abnormal behaviors, the spatio-temporal feature matching method is used to identify the abnormal patterns based on the filtered abnormal trajectories. Experimental results showed that the trajectory-based abnormal behavior discovery method can realize a rapid discovery of abnormal trajectories and effective judgment of abnormal behavior patterns.
Highlights
IntroductionPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
This paper investigated the relationship between abnormal behavior and personal trajectory data and proposed a new method for identifying abnormal behavior with trajectory data
Abnormal behaviors were divided into four categories, including wandering, scouting, random walking, and trailing
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The current situation of security prevention and control is extremely severe and normal. Video surveillance methods are mainly used to discover abnormal behaviors and obtain evidence afterwards. Numerous studies on abnormal behaviors have been conducted using video data [1,2,3,4,5,6,7,8,9]. Rai et al [7] defined the type of motion trajectory and identified the abnormal behavior trajectory by the motion history images and moments. Pathak et al [8] used probabilistic topic model Probabilistic Latent Semantic
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