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Title	Real-Time Anomaly Detection For Smart And Safe City Using Spatiotemporal Deep Learning
ID_Doc	44307
Authors	Hasib R.; Jan A.; Khan G.M.
Year	2022
Published	2nd IEEE International Conference on Artificial Intelligence, ICAI 2022
DOI	http://dx.doi.org/10.1109/ICAI55435.2022.9773464
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. © 2022 IEEE.
Author Keywords	anomaly detection; computer vision; intelligent video surveillance; spatio-temporal feature extraction

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