With the popularization of the city monitoring system, surveillance videos have been increasingly presented. Traditional methods for video analytic require professionals to monitor the video constantly to find out abnormal events, which leads to a tough and timeconsuming task. Therefore, research activities on automatic video anomaly detection are of great practical significance since a feasible detection technique can reduce the large amount of human resources used for monitoring videos. This thesis presents several novel deep learning methods for video anomaly detection. In addition, it provides a potential system for the application of these methods and extension of the video sources. Video anomaly detection is a problem of detecting and classifying anomalies in videos. Anomaly refers to an unusual event or emergency that deviates from what is standard, normal and expected. The kernel of video anomaly detection is the extraction of spatial and temporal features.