Abnormal event detection is a hot topic in the field of intelligent surveillance monitoring technology research currently. As an important research content of computer vision， its main goal is to use computers to automatically detect abnormal events. Traditional methods have limitations in the weakness of low-level video feature description ability， high computational cost of anomaly detection methods and poor robustness in modeling complex scenes. In recent years， how to design a high-level semantic feature extraction method， accelerate the process of abnormal event detection， and model complex scenes such as multiple cameras have become the forefront topic of current research. Based on the research situation at home and abroad and the mainstream methods， this paper introduces the basic techniques involved in abnormal event detection in surveillance videos， and analyzes the advantages and disadvantages of various types of surveillance video feature extraction methods， feature learning models and anomaly detection methods. This paper also summarizes the commonly used benchmark datasets that can be used to abnormal event detection in surveillance videos. Finally， we discuss the difficulties， challenges and future development trends of abnormal event detection in surveillance videos.