Anomaly detection in surveillance videos is a challenging problem in computer vision community. In this paper, a novel unsu-pervised learning framework is proposed to detect and localize abnormal events in real-time manner. Typical methods mainly rely on extracting complex handcraft features and learning only a fitting model for prediction. In contrast, normal events are represented using simple spatio-temporal volume (STV) in our method, then adaptive multiple auto-encoders (AMAE) are constructed to handle the inter-class variation in normal events. When testing on an unknown frame, reconstruction errors of multiple auto-encoders are utilized for prediction. Experiments are performed on UCSD Ped2 and UMN datasets. Experimental results show that our method is effective to detect and localize abnormal events at a speed of 70 fps.
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