abnormal event detection in video surveillance is extremely important, especially for crowded scenes. in recent years, many algorithms have been proposed based on hand-crafted features. however, it still remains challenging to decide which kind of feature is suitable for a specific situation. in addition, it is hard and time-consuming to design an effective descriptor. in this paper, video events are automatically represented and modeled in unsupervised fashions. specifically, appearance and motion features are simultaneously extracted using a pcanet from 3d gradients. in order to model event patterns, a deep gaussian mixture model (gmm) is constructed with observed normal events. the deep gmm is a scalable deep generative model which stacks multiple gmm-layers on top of each other. as a result, the proposed method acquires competitive performance with relatively few parameters. in the testing phase, the likelihood is calculated to judge whether a video event is abnormal or not. in this paper, the proposed method is verified on two publicly available datasets and compared with state-of-the-art algorithms. experimental results show that the deep model is effective for abnormal event detection in video surveillance.