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dc.contributor.advisorMoodley, Deshendran.
dc.contributor.advisorPillay, Anban Woolaganathan.
dc.creatorClark, Andrew William.
dc.date.accessioned2020-02-13T13:03:57Z
dc.date.available2020-02-13T13:03:57Z
dc.date.created2018
dc.date.issued2018
dc.identifier.urihttps://researchspace.ukzn.ac.za/handle/10413/16919
dc.descriptionMasters Degree. University of KwaZulu-Natal, Durban.en_US
dc.description.abstractCamera-based hand gesture recognition for interaction in virtual reality systems promises to provide a more immersive and less distracting means of input than the usual hand-held controllers. It is unknown if a camera would effectively distinguish hand poses made in a virtual reality environment, due to lack of research in this area. This research explores and measures the effectiveness of static hand pose input with a depth camera, specifically the Leap Motion controller, for user interaction in virtual reality applications. A pose set was derived by analyzing existing gesture taxonomies and Leap Motion controller-based virtual reality applications, and a dataset of these poses was constructed using data captured by twenty-five participants. Experiments on the dataset utilizing three popular machine learning classifiers were not able to classify the poses with a high enough accuracy, primarily due to occlusion issues affecting the input data. Therefore, a significantly smaller subset was empirically derived using a novel algorithm, which utilized a confusion matrix from the machine learning experiments as well as a table of Hamming Distances between poses. This improved the recognition accuracy to above 99%, making this set more suitable for real-world use. It is concluded that while camera-based pose recognition can be reliable on a small set of poses, finger occlusion hinders the use of larger sets. Thus, alternative approaches, such as multiple input cameras, should be explored as a potential solution to the occlusion problem.en_US
dc.language.isoenen_US
dc.subject.otherHand gesture recognition.en_US
dc.subject.otherVirtual reality.en_US
dc.subject.otherMachine learning.en_US
dc.subject.otherDepth camera.en_US
dc.subject.otherArtificial Intelligence.en_US
dc.titleAn evaluation of depth camera-based hand pose recognition for virtual reality systems.en_US
dc.typeThesisen_US


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