Detecting contextual anomalies of crowd motion in surveillance video

Abstract

Many works have been proposed on detecting individual anomalies in crowd scenes, i.e., human behaviors anomalous with respect to the rest of the behaviors. In this paper, we introduce a new concept of contextual anomaly into the field of crowd analysis, i.e., the behaviors themselves are normal but they are anomalous in a specific context. Our system follows an unsupervised approach. It automatically discovers important contextual information from the crowd video and detects the blobs corresponding to contextually anomalous behaviors. Our experiments show that the approach works well in detecting contextual anomalies from crowd video with different motion contexts.