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Frame prediction based on AutoEncoder plays a significant role inunsupervised video anomaly detection. Ideally, the models trained on the normaldata could generate larger prediction errors of anomalies. However, thecorrelation between appearance and motion information is underutilized, whichmakes the models lack an understanding of normal patterns. Moreover, the modelsdo not work well due to the uncontrollable generalizability of deepAutoEncoder. To tackle these problems, we propose a multi-levelmemory-augmented appearance-motion correspondence framework. The latentcorrespondence between appearance and motion is explored via appearance-motionsemantics alignment and semantics replacement training. Besides, we alsointroduce a Memory-Guided Suppression Module, which utilizes the differencefrom normal prototype features to suppress the reconstruction capacity causedby skip-connection, achieving the tradeoff between the good reconstruction ofnormal data and the poor reconstruction of abnormal data. Experimental resultsshow that our framework outperforms the state-of-the-art methods, achievingAUCs of 99.6\%, 93.8\%, and 76.3\% on UCSD Ped2, CUHK Avenue, and ShanghaiTechdatasets.

Multi-level Memory-augmented Appearance-Motion Correspondence Framework for Video Anomaly Detection