Human action recognition in videos is a challenging topic in the field of computer vision.It is widely not only used in video information retrieval

daily life security

public video surveillance

but also human-computer interaction

scientific cognition and other fields.First

the research background

research significance and difficulties of action recognition are briefly introduced

and then the deep learning model based action recognition methods are comprehensively reviewed from three different aspects:the types and numbers of input signals

the combination with traditional feature extraction methods

and the pre-trained datasets.Furthermore

the performances of some typical methods on UCF101 and HMDB51 datasets are overviewed and analyzed.Last the possible future research directions are discussed from three perspectives:the video data preprocessing

the video human motion feature representation

and the model training.