Research on Abnormal Pedestrian Trajectory Detection of Dynamic Crowds in Public Scenarios

In public scenes such as stations and hospitals, the crowds are intensive and abnormal pedestrian often causes group hazards. The recognition of abnormal pedestrian is an important security problem, which is generally solved by inspection robots. Traditional visual feature methods pay much attention to the inherent attributes of pedestrians (such as gender and age), which ignores the complex semantic information displayed by pedestrian trajectories. This article uses scene monitoring visual sensors to analyze pedestrian trajectories in public scenes. We propose an abnormal trajectory recognition framework, which analyzes the pedestrian trajectories from clusters, deviation and trajectory entropy. In this framework, the convergence condition of the K-Means method is optimized to cluster the pedestrian destinations and trajectories; the Mahalanobis distance is used to evaluate the trajectory deviation; the dimensional feature is established through the velocity and angle difference of the trajectory. In the end, the results can prove that the methods in this article can successfully identify abnormal pedestrians.