Online Learning with Self-Organizing Maps for Anomaly Detection in Crowd Scenes

Abstract

Detecting abnormal behaviors in crowd scenes is quite important for public security and has been paid more and more attentions. Most previous methods use offline trained model to perform detection which can't handle the constantly changing crowd environment. In this paper, we propose a novel unsupervised algorithm to detect abnormal behavior patterns in crowd scenes with online learning. The crowd behavior pattern is extracted from the local spatio-temporal volume which consists of multiple motion patterns in temporal order. An online self-organizing map (SOM) is used to model the large number of behavior patterns in crowd. Each neuron can be updated by incrementally learning the new observations. To demonstrate the effectiveness of our proposed method, we have performed experiments on real-world crowd scenes. The online learning can efficiently reduce the false alarms while still be able to detect most of the anomalies.