Learning Temporal Context of Normality for Unsupervised Anomaly Detection in Videos

Incomplete reconstruction of abnormal samples using convolutional autoencoders trained only on normal samples has been the key principle of anomaly detection. Such detection mechanisms utilize reconstruction error differences between normal and abnormal frames. This is not consistent, however, causing the normal and abnormal samples undistin-guishable. To handle this problem, we propose a shuffle-and-sort strategy for learning the temporal context of normality. The purpose of the strategy is to reconstruct shuffled input frames into an output with the correct order using a self-attention mechanism. Consequently, the proposed method can model the temporal context of normal events, which prevents the successful completion of reconstructing anomalies by the convolutional layers. We demonstrated the detection efficiency of the proposed method using public benchmark datasets: UCSD Pedestrian 2, CUHK Avenue, and ShanghaiTech Campus Datasets.