With the continuous development of monitoring technology, surveillance cameras have been widely deployed in various scenarios. Manual detection of video abnormality has become impossible. Therefore, video anomaly detection technology, as the core of intelligent surveillance systems, is receiving extensive attention and research. With the development of deep learning, the field of video anomaly detection has made significant achievements and has emerged many new anomaly detection methods. Unsupervised and weakly supervised video anomaly detection learning methods applied to various data types were sorted out, the contributions of existing methods were analyzed, and the performance of different models was compared. In addition, some commonly used and newly released datasets have also been compiled, and the challenges and development trends that future work will face have been summarized.