Video human body interaction motion identification method based on optical flow graph depth learning model

The invention discloses a video human body interaction motion identification method based on an optical flow graph depth learning model. The method mainly comprises steps that step 1, deframing of a test set video and a training set video is carried out, an optical flow sequence graph is calculated through utilizing two adjacent frames; step 2, the optical flow sequence graph is pre-processed, and optical flow graphs with relatively few information quantity are deleted; step 3, a residual error neural network is trained through utilizing the training set optical flow sequence acquired in the step 2, the test set and training set optical flow graph sequences are taken as input, and spatial domain characteristics are acquired; step 4, a long and short memory model is trained through utilizing training set characteristics, test set characteristics are inputted to acquire each type of probability output; and step 5, a classification result is acquired through employing voting model statistics. The method is advantaged in that relevant patent blanks are filled through utilizing the depth learning model to carry out human body motion identification, identification accuracy is high, and the method is applicable to multiple occasions.