An evaluation of depth camera-based hand pose recognition for virtual reality systems.
| dc.contributor.advisor | Moodley, Deshendran. | |
| dc.contributor.advisor | Pillay, Anban Woolaganathan. | |
| dc.contributor.author | Clark, Andrew William. | |
| dc.date.accessioned | 2020-02-13T13:03:57Z | |
| dc.date.available | 2020-02-13T13:03:57Z | |
| dc.date.created | 2018 | |
| dc.date.issued | 2018 | |
| dc.description | Masters Degree. University of KwaZulu-Natal, Durban. | en_US |
| dc.description.abstract | Camera-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.identifier.uri | https://researchspace.ukzn.ac.za/handle/10413/16919 | |
| dc.language.iso | en | en_US |
| dc.subject.other | Hand gesture recognition. | en_US |
| dc.subject.other | Virtual reality. | en_US |
| dc.subject.other | Machine learning. | en_US |
| dc.subject.other | Depth camera. | en_US |
| dc.subject.other | Artificial Intelligence. | en_US |
| dc.title | An evaluation of depth camera-based hand pose recognition for virtual reality systems. | en_US |
| dc.type | Thesis | en_US |