Classroom behavior detection method based on improved Openpose model and facial micro-expressions

Abstract

The invention discloses a classroom behavior detection method based on an improved Openpose model and facial micro-expressions, and the method comprises the steps: arranging a camera in front of a desk, and detecting the classroom behaviors of students in real time; and recognizing facial information and upper body skeleton information through an artificial intelligence model, taking whether key points can be recognized or not and the distance between the key points as main judgment conditions, serving changes of micro-expressions as auxiliary judgment conditions, if a certain student does notmeet the condition for a period of time, judging that the examination behavior of the student is abnormal. Besides, through the video stream of one class, the possible stage of abnormal behaviors ofstudents is found out and analyzed, and innovation and reform of teaching are realized. Interference factors are reduced through machine vision recognition, equipment is simplified, and meanwhile theinvention further provides a corresponding data analyzing and processing system. According to the invention, the network model is further optimized through adoption of methods such as residual network, weight trimming and the like. According to the invention, self-service classroom behavior detection and feedback are realized, the test efficiency is high, and the accuracy can reach 95%.

Description

technical field [0001] The invention relates to the technical fields of deep learning, machine vision and image processing, in particular to a classroom behavior detection method based on an improved Openpose model and facial micro-expressions. Background technique [0002] In recent years, intelligent technologies represented by deep learning have penetrated into various fields of people's lives, and have entered the stage of large-scale application. In 2017, the State Council proposed in the "New Generation Artificial Intelligence Development Plan" issued: "Use intelligent technology to accelerate the reform of talent training models and teaching methods, and build a new education system that includes intelligent learning and interactive learning." [0003] The classroom has long been a focus of educational research. As the main body of teaching activities, students' behavior directly reflects the real situation of the classroom. Therefore, classroom student behavior ana...

AI Technical Summary

Problems solved by technology

Under the increasingly higher requirements for the test accuracy of the items in the test and the realization of unmanned test requirements, the existing classroom behavior methods (Tan Bin, Yang Shuhan. Research on student classroom behavior detection algorithm based on Faster R-CNN[J ].Modern Computer (Professional Edition) (33):47-49.), (Liao Peng, Liu Chenming, Su Hang, et al. Abnormal Behavior Detection and Analysis System Based on Deep Learning in Classroom[J]. Electronic World (8):97-98.) There is no good test experience yet, and the test efficiency, accuracy, and degree of automation still need to be improved

Moreover, classroom behavior detection methods are error-prone, labor-intensive, and individual judgment standards affect test results

The existing analysis methods based on machine vision mainly use convolutional neural network for image recognition. However, the traditional convolutional neural network is not satisfactory for the recognition accuracy and speed of student behavior in the application of classroom behavior recognition, and In the process of classroom behavior recognition, it is also very difficult to obtain a large number of training samples

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