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Recent anomaly detection algorithms have shown powerful performance byadopting frame predicting autoencoders. However, these methods face twochallenging circumstances. First, they are likely to be trained to beexcessively powerful, generating even abnormal frames well, which leads tofailure in detecting anomalies. Second, they are distracted by the large numberof objects captured in both foreground and background. To solve these problems,we propose a novel superpixel-based video data transformation technique namedRandom Superpixel Erasing on Moving Objects (RandomSEMO) and Moving Object Loss(MOLoss), built on top of a simple lightweight autoencoder. RandomSEMO isapplied to the moving object regions by randomly erasing their superpixels. Itenforces the network to pay attention to the foreground objects and learn thenormal features more effectively, rather than simply predicting the futureframe. Moreover, MOLoss urges the model to focus on learning normal objectscaptured within RandomSEMO by amplifying the loss on the pixels near the movingobjects. The experimental results show that our model outperformsstate-of-the-arts on three benchmarks.

RandomSEMO: Normality Learning Of Moving Objects For Video Anomaly Detection