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Title	Space-Time Attention Model-Based Anomalous Event Classification For Smart City Applications
ID_Doc	52352
Authors	Nayak R.; Pati U.C.; Kumar Das S.
Year	2024
Published	Intelligent Computing and Emerging Communication Technologies, ICEC 2024
DOI	http://dx.doi.org/10.1109/ICEC59683.2024.10837247
Abstract	Anomalous event classification automatically identifies anomalous events using the videos in an intelligent video surveillance system. However, anomalous event classification is challenging due to inherent research challenges such as the requirement of high-end computational infrastructure, data imbalances, and data scarcity. Typically, a combination of Convolution Neural Networks (CNNs) and Long-Short-Term-Memory (LSTM) are used to model the spatiotemporal dynamics of the videos for video classification. However, these models have no attention mechanism to boost the relevant spatiotemporal features and discard the irrelevant features. Hence, a Space-Time Attention Model (STAM)-based anomalous event classifier is proposed. The model is trained and validated on the "Anomalous Event Classification 22,"i.e., the "AEC22 dataset"comprising twenty-two anomalous event classes such as abuse, arrest, arson, assault, etc. The STAM is a combined spatial and temporal transformer that takes a series of frames extracted from the input video and predicts corresponding video-level classification as the output. Subsequently, the proposed model provides 92.84% classification accuracy, which is compared with the two state-of-the-art video classification methods to validate its superiority. The proposed model has huge potential for classifying anomalous events in smart city applications. © 2024 IEEE.
Author Keywords	3D-CNN; Anomalous event classification; Convolutional LSTM; DenseNet; LSTM; ResNet; Space-Time Attention Module; Video classification

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