Detecting Abnormality Without Knowing Normality: A Two-Stage Approach for Unsupervised Video Abnormal Event Detection

Abnormal event detection in video surveillance is a valuable but challenging problem. Most methods adopt a supervised setting that requires collecting videos with only normal events for training. However, very few attempts are made under unsupervised setting that detects abnormality without priorly knowing normal events. Existing unsupervised methods detect drastic local changes as abnormality, which overlooks the global spatio-temporal context. This paper proposes a novel unsupervised approach, which not only avoids manually specifying normality for training as supervised methods do, but also takes the whole spatio-temporal context into consideration. Our approach consists of two stages: First, normality estimation stage trains an autoencoder and estimates the normal events globally from the entire unlabeled videos by a self-adaptive reconstruction loss thresholding scheme. Second, normality modeling stage feeds the estimated normal events from the previous stage into one-class support vector machine to build a refined normality model, which can further exclude abnormal events and enhance abnormality detection performance. Experiments on various benchmark datasets reveal that our method is not only able to outperform existing unsupervised methods by a large margin (up to 14.2% AUC gain), but also favorably yields comparable or even superior performance to state-of-the-art supervised methods.