Learning behaviour recognition based on multi-object image in single viewpoint

Abstract

Though greatly significant for education evaluation and improvement the analysis of learning behaviour of students is, recognition of behaviour of students in the classroom surveillance based on one single image remains a challenging work due to problems including heavy occlusion, low resolution, small target size, large variability of camera viewpoints and significant perspective effect. In this paper, an innovative multiple-model method for recognizing students’ learning behaviour and counting the number of students in classroom scenes via detecting individuals’ head, which divides students’ learning behaviour into three categories according to the head posture, is proposed. Through viewing heads at different postures as targets of different categories, behaviour recognition problem is transformed into multi-target detection problem. As the density of students in different images varies dramatically, a multiple model consisting of three detection networks with different sized receptive fields and one switch net is applied that each detection network selects a large number of sub-windows with small stride size from input images to optimize the performance of the model in classroom scenes while the switch net is trained to predict the density of students in the input image and relays the classroom image to the corresponding detection network according to the students’ density prediction. This method is obtained to outperform other methods in terms of accuracy and speed in comparison with state-of-the-art methods on a real classroom surveillance dataset.