Research On The Algorithm Of Students' Classroom Behavior Detection Based On Video

With the development of science and technology,more and more industries have the existence of video monitoring.Through video monitoring,we can observe the situation in the designated environment and analyze it accordingly.Video based behavior detection engineering,community and other industries have been widely used,but the development in the education industry is relatively slow.The computer technology is used to analyze students' class video,combine object detection and behavior classification to detect students' class behavior,can reduce teachers' teaching pressure,so they can devote more attention to their own teaching work.In addition,teachers and students can view the corresponding behavior analysis results after class,which can better guide students to listen carefully,so as to make the teaching effect of teachers better and increase the efficiency of students.Based on the above situation,this paper focuses on the analysis and research of video based classroom behavior detection algorithm,combine target detection and behavior recognition to achieve behavior detection.The research content is divided into three parts as follows:Firstly,study the behavior classification algorithm,make the target detection data set according to the characteristics of the subject.Through the analysis of application scenarios and identification requirements,select yolo-v3 algorithm as the basic algorithm.Based on yolo-v3 algorithm,improve the weight and loss calculation of loss function,In addition,improve the network structure by adding multi-scale features,and finally obtain the yolo-op4 algorithm as target detection algorithm of the papar.After that,compare the yolo algorithm with the current deep learning algorithm,test the performance of yolo-op4 algorithm.The test results show that yolo-op4 algorithm has good performance in the data set of the project.Then,Research on behavior classification algorithm,study the image processing and enhancement.According to the characteristics of the video image of the project,design a variety of students' classroom behaviors,Combining the designed behaviors with image enhancement,design a data set of student behavior classification.Analyze the scenarios and requirements of subject behavior classification,Based on VGG-16 algorithm,design VGG-M algorithm by network optimization and multi-scale feature fusion as the behavior classification algorithm in this paper.And test the performance of traditional SVM,KNN classification algorithm and VGG-16 algorithm,VGG-M algorithm.then combine the image processing algorithm with VGG-16 algorithm and VGG-M algorithm and test them on the behavioral classification data set.The testresults show that VGG-M algorithm is better than VGG-16 algorithm in behavior classification,and the training speed is faster than VGG-16 algorithm.Finally,based on the algorithm designed in this paper,design and implement the student classroom behavior detection system.In order to verify the feasibility and effectiveness of the methods involved in this paper,use java Web and python technology,combine target detection with behavior classification and implement a student classroom behavior detection system based on the trained recognition model,which successfully detects and analyzes the student's behavior.This paper divides and saves the students' non serious learning behaviors for the convenience of teachers' viewing,analyzes the classroom learning situation through the proportion of students' non serious learning behaviors in the total behaviors,generates corresponding charts.Finally,test the main functions of the system.