COVAD: Content-oriented video anomaly detection using a self attention-based deep learning model

Abstract

BackgroundVideo anomaly detection has always been a hot topic and has attracted increasing attention. Many of the existing methods for video anomaly detection depend on processing the entire video rather than considering only the significant context. MethodThis paper proposes a novel video anomaly detection method called COVAD that mainly focuses on the region of interest in the video instead of the entire video. Our proposed COVAD method is based on an autoencoded convolutional neural network and a coordinated attention mechanism, which can effectively capture meaningful objects in the video and dependencies among different objects. Relying on the existing memory-guided video frame prediction network, our algorithm can significantly predict the future motion and appearance of objects in a video more effectively. ResultThe proposed algorithm obtained better experimental results on multiple datasets and outperformed the baseline models considered in our analysis. Simultaneously, we provide an improved visual test that can provide pixel-level anomaly explanations.