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

Abstract

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.

Description

technical field [0001] The invention relates to a method for recognizing human body interaction actions in videos, in particular to a method for recognizing human body interaction actions in videos based on an optical flow graph deep learning model. Background technique [0002] As technology continues to evolve, so does the need to understand video content. The widespread use of cameras has resulted in more and more video information. However, it is difficult to manually process these massive amounts of information, so relevant methods are needed to analyze the content in the video. In the field of intelligent monitoring, the recognition of human interaction is particularly important. Unexpected events such as fights, abnormal behavior detection, etc. all rely on the accurate identification of human interaction actions. Therefore, accurate human interaction action recognition has important social significance. [0003] Video content understanding aims to allow computers...

AI Technical Summary

Problems solved by technology

The above-mentioned technical solution still adopts the traditional manual feature method, and there is still a gap in the relevant patents on the use of deep learning models for human interaction action recognition in the video field

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