Aiming at the problem of false alarms caused by the low probability of abnormal events in existing anomaly detection methods, a novel video anomaly detection approach is proposed based on the Gaussian process regression framework. By integrating the structural properties of deep learning with the flexibility of kernel methods, a new deep learning technology called deep Gaussian process regression that fully encapsulates CNN structure is introduced to extract features and detect anomaly in one model. The results on the popular Avenue dataset and on a recently introduced realevent video surveillance dataset show that the detection model based on deep Gaussian process regression has achieved 839% framelevel AUC and 344% framelevel AUC on the two dataset, respectively, and has reached the state of the art in performance.