Enhancing colour-coded poll sheets using computer vision as a viable Audience Response System (ARS) in Africa

Abstract

Audience Response Systems (ARS) give a facilitator accurate feedback on a question posed to the listeners. The most common form of ARS are clickers; Clickers are handheld response gadgets that act as a medium of communication between the students and facilitator. Clickers are prohibitively expensive creating a need to innovate low-cost alternatives with high accuracy. This study builds on earlier research by Gain (2013) which aims to show that computer vision and coloured poll sheets can be an alternative to clicker based ARS. This thesis examines a proposal to create an alternative to clickers applicable to the African context, where the main deterrent is cost. This thesis studies the computer vision structures of feature detection, extraction and recognition. In this research project, an experimental study was conducted using various lecture theatres with students ranging from 50 - 150. Python and OpenCV tools were used to analyze the photographs and document the performance as well as observing the different conditions in which to acquire results. The research had an average detection rate of 75% this points to a promising alternative audience response system as measured by time, cost and error rate. Further work on the capture of the poll sheet would significantly increase this result. With regards to cost, the computer vision coloured poll sheet alternative is significantly cheaper than clickers.