HMM-based Scheme for Smart Instructor Activity Recognition in a Lecture Room Environment

Abstract

Instructor activity recognition can certainly play its part as an important parameter in evaluating and improving the performance of an instructor. This paper presents a single-layered sequential approach for instructor activity recognition in the lecture room environment. A hidden Markov model (HMM) scheme is selected as a sequential approach for activity recognition. The proposed system incorporates the five major activities of the instructor in the lecture room, i.e. walking, writing, pointing towards the board, standing, and pointing towards presentations. Background/foreground modelling is carried out using a Gaussian mixture model (GMM) for instructor detection in the lecture room. Mesh features are selected to represent the instructor. After vector quantization, features are passed to the HMM for activity recognition. Time is tracked, and the occurrences of each activity are counted to elaborate on the activities the instructor performed during the lecture. The proposed scheme proved to be efficient owing to its high accuracy rate of over 90 percent in recognizing five different activities of an instructor as tested in a MATLAB simulation environment.