Behavior recognition is developing rapidly,and a number of behavior recognition algorithms based on deep network automatic learning features have been proposed.The deep learning method requires a large number of data to train,and requires higher computer storage and computing power.After a brief review of the current popular behavior recognition method based on deep network,it focused on the traditional behavior recognition methods.Traditional behavior recognition methods usually followed the processes of video feature extraction,modeling of features and classification.Following the basic process,the recognition process was overviewed according to the following steps,feature sampling,feature descriptors,feature processing,descriptor aggregation and vector coding.At the same time,the benchmark data set commonly used for evaluating the algorithm performance was also summarized.