An Hierarchical Framework for Classroom Events Classification

Abstract

In this paper, a model for classroom events classification is proposed. Major classroom events which are considered in this work are drowsiness of a student, group discussion, steady and alert, and noisy classroom. These events are classified using a two level classification model. It makes use of simple threshold based classifiers for classification. In the first level, classes such as noisy classroom and drowsiness are separated from that of remaining classes based on global threshold. The global threshold is computed based on correlation coefficients, computed across the intensity values of the video frames. The correlation scores obtained from each video are used for classification. During second level, a partially labeled video is classified as a member of any of the said four classes based on the local threshold computed from each class of videos. Local threshold is computed based on the global characteristics extracted from the videos. For classification purpose, the events which are considered here are strictly mutually exclusive events. Due to the lack of classroom events video datasets, the dataset has been created consisting of 96 videos spread across 4 different classes. The proposed model is validated using suitable validity measures viz., accuracy, precision, recall, and f-measure. The results show that the proposed model performs better in classifying the said events.