Affiliations: School of Foreign Languages, Tianshui Normal University, Gansu, Tianshui, China
Correspondence:
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Corresponding author. Huifang Cao, School of Foreign Languages, Tianshui Normal University, Gansu, Tianshui 730070, China. E-mail: tschfk@sina.com.
Abstract: At present, English teaching does not play the role of a smart classroom, and it is difficult to grasp the student status and characteristics in real time in actual teaching. Based on this, starting from the video image and static image and the actual situation of English classroom teaching, this study, based on the convolutional neural network and random forest algorithm, performs static image human behavior recognition under different image representation conditions, and studies the influence of background information of image and spatial distribution information of image features on recognition accuracy. Then, based on the similarity between different behavior classes, a static image human body behavior recognition method based on improved random forest is proposed. In addition, through theoretical research, an algorithm model that can identify the characteristics of English classrooms is constructed, and the static and dynamic images of English teaching are taken as an example to conduct experimental analysis. The research shows that the proposed method has certain effects and can provide theoretical reference for subsequent related research.
Keywords: Convolutional neural network, random forest, static image, English classroom, feature recognition
