Real-Time Anomaly Detection for Smart and Safe City Using Spatiotemporal Deep Learning

Abstract

A smart city ensures the safety of its citizens by the reduction of crime and terror threats. Despite intensive efforts to prevent and control anomalous human activities, they still pose a major risk and challenge to the society. This paper presents an automatic recognition of unusual human behavior captured by a CCTV camera in public areas, using spatio-temporal 3D convolutional neural networks. The weakly labeled benchmark dataset has been properly annotated to remove noise for accurately localizing anomalies within videos. This human-related dataset with real crime scenes is then compared to other state-of-the-art techniques such as Pseudo 3D and ResNet 3D. Our experimental results on the newly developed dataset outperforms most competing models in terms of area under the curve (AUC), obtaining 97.39% AUC.