AONet: Attention network with optional activation for unsupervised video anomaly detection

Abstract

Anomaly detection in video surveillance is crucial but challenging due to the rarity of irregular events and ambiguity of defining anomalies. We propose a method called AONet that utilizes a spatiotemporal module to extract spatio-temporal features efficiently, as well as a residual autoencoder equipped with an attention network for effective future frame prediction in video anomaly detection. AONet utilizes a novel activation function called OptAF that com-bines the strengths of the ReLU, leaky ReLU, and sigmoid functions. Further-more, the proposed method employs a combination of robust loss functions to address various aspects of prediction errors and enhance training effectiveness. The performance of the proposed method is evaluated on three widely used benchmark datasets. The results indicate that the proposed method outper-forms existing state-of-the-art methods and demonstrates comparable perfor-mance, achieving area under the curve values of 97.0%, 86.9%, and 73.8% on the UCSD Ped2, CUHK Avenue, and ShanghaiTech Campus datasets, respec-tively. Additionally, the high speed of the proposed method enables its applica-tion to real-time tasks.

Keywords

• activation function
• convolutional neural network
• loss function
• unsupervised learning
• video anomaly detection