Research On The Analysis Of Behavior Status In Classes Based On Natural Environment

In basic education,mastering the learning status of students in classes timely and accurately is conductive to providing information reference and overall assessment in real time for teachers and management personnel,which is of great value for educational application.At present,there are many information techniques being used in the analysis of behavior status of students in classes,moreover,the analysis techniques of status based on class video are featured by strong timeliness,wide dimension,large capacity and so on.In this regard,they are especially suitable for acquiring the analysis data related to the status of students in classes,so the various educational technology companies at home and abroad pay close attention to them.However,there are the following problems existing in the acquisition technology of status of students in classes based on video analysis at present.Firstly,it lacks of video analysis method for large scene,such as the classrooms in primary and middle schools;secondly,face detection is easily affected by the light background and detection target,so it is hard to transfer the technology in classes on the premise of high accuracy and speed;at last,most of the technologies for the analysis of behavior status in classes based on video highlight the single behavior feature as facial expression or the head status of students,which makes them hard to reflect the behavior status of students in classes comprehensively.To tackle the above problems,the paper mainly carries out the following research work:(1)The face detection technology for complicated environment can not balance between the accuracy and speed of detection,in this regard,the paper proposes a double-layer face detection algorithm based on mixed framework by combining with the special features in classroom scenes.The method adjusts between the rough face detection algorithm and the fine face detection algorithm in accordance with the different situations of students' faces.In terms of the rough face detection algorithm,it combines the face features with AdaBoost cascade algorithm;in terms of the fine face detection algorithm,it is formed after being trained by convolutional neural network.Such double-layer face detection algorithm is able to keep accuracy while enhancing speed,so it lays a good foundation for the collection and analysis of the behavior features of students in the future.(2)The paper also proposes to collect the two features of students as facial expression and head pose,namely the method for the analysis of behavior status in classes which integrates the attention and emotion of students in accordance with the problem that the current study just highlights the single feature of facial expression when it comes to the analysis of behavior status of students.Among them,the recognition model for facial expression established by using convolutional neural network divides the recognized facial expressions into two types as positive emotion and negative emotion.Then,it judges the attention of students by comparing the differences between the head pose of targeted student and those of the surrounding students,which helps to enhance the relevant reliability.(3)The method for the analysis of behavior status proposed in the paper can be used to verify and analyze the teaching video collected by the camera,then it presents the key data by charts so that teachers are able to timely obtain the behavior status of students.By the experiments,the feasibility and accuracy of the system have been verified,moreover,the advantages,disadvantages and points to be improved in the system have also been analyzed.