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

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dc.contributor.advisorGain, Jamesen_ZA
dc.contributor.advisorMarais, Patricken_ZA
dc.contributor.authorMuchaneta, Irikidzai Zorodzaien_ZA
dc.date.accessioned2018-04-24T14:01:51Z
dc.date.available2018-04-24T14:01:51Z
dc.date.issued2018en_ZA
dc.description.abstractAudience 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.en_ZA
dc.identifier.apacitationMuchaneta, I. Z. (2018). <i>Enhancing colour-coded poll sheets using computer vision as a viable Audience Response System (ARS) in Africa</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Computer Science. Retrieved from http://hdl.handle.net/11427/27854en_ZA
dc.identifier.chicagocitationMuchaneta, Irikidzai Zorodzai. <i>"Enhancing colour-coded poll sheets using computer vision as a viable Audience Response System (ARS) in Africa."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Computer Science, 2018. http://hdl.handle.net/11427/27854en_ZA
dc.identifier.citationMuchaneta, I. 2018. Enhancing colour-coded poll sheets using computer vision as a viable Audience Response System (ARS) in Africa. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Muchaneta, Irikidzai Zorodzai AB - 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. DA - 2018 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2018 T1 - Enhancing colour-coded poll sheets using computer vision as a viable Audience Response System (ARS) in Africa TI - Enhancing colour-coded poll sheets using computer vision as a viable Audience Response System (ARS) in Africa UR - http://hdl.handle.net/11427/27854 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/27854
dc.identifier.vancouvercitationMuchaneta IZ. Enhancing colour-coded poll sheets using computer vision as a viable Audience Response System (ARS) in Africa. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Computer Science, 2018 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/27854en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Computer Scienceen_ZA
dc.publisher.facultyFaculty of Scienceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherInformation Technologyen_ZA
dc.titleEnhancing colour-coded poll sheets using computer vision as a viable Audience Response System (ARS) in Africaen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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