Graph stream mining based anomalous event analysis

Abstract

© Springer Nature Switzerland AG 2018. A major challenge in video surveillance is how to accurately detect anomalous behavioral patterns that may indicate public safety incidents. In this work, we address this challenge by proposing a novel architecture to translate the crowd status problem in videos into a graph stream analysis task. In particular, we integrate crowd density monitoring and graph stream mining to identify anomalous crowd behavior events. A real-time tracking algorithm is proposed for automatic identification of key regions in a scene, and at the same time, the pedestrian flow density between each pair of key regions is inferred over consecutive time intervals. These key regi..