Research on human behavior recognition based on CNN features and HMM time series modeling

• 1、Harbin Engineering University College of information and Communication Engineering,Harbin,150001

Abstract：With the rapid growth of the total number and category of video, intelligent video analysis has become the core technology in many practical applications, such as anomaly monitoring, medical diagnosis and video retrieval. People are the main body of the society, so most of the videos are about the contents of human behavior, and the recognition of human behavior has become the core technology of video analysis. At present, the technology of image recognition is mature, and video can be regarded as an image sequence, so the analysis of video can be converted into image processing. Deep learning is the most popular method in the field of image processing. The CNN model has achieved very good results, which has great advantages compared with some traditional manual feature extraction methods. Video is a signal with time sequence information, so better use of time sequence information in classification recognition can greatly improve the accuracy of recognition. In this paper, a human behavior recognition method is proposed, which uses CNN to extract the image features, and then applies HMM to sequence the image feature sequence, and innovatively uses the parameters of HMM as the overall feature representation of the video. Finally, the method of this paper is verified on the public human behavior database, and the accuracy of recognition is very high.

Keywords： action recognition CNN feature extraction HMM feature representation