A system for pose analysis and selection in virtual reality environments

Clark, A and Pillay, AW and Moodley, D (2020) A system for pose analysis and selection in virtual reality environments, Proceedings of SAICSIT '20: Conference of the South African Institute of Computer Scientists and Information Technologists 2020, 14-16 September, 2020, ACM.

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Depth cameras provide a natural and intuitive user interaction mechanism in virtual reality environments by using hand gestures as the primary user input. However, building robust VR systems that use depth cameras are challenging. Gesture recognition accuracy is affected by occlusion, variation in hand orientation and misclassification of similar hand gestures. This research explores the limits of the Leap Motion depth camera for static hand pose recognition in virtual reality applications. We propose a system for analysing static hand poses and for systematically identifying a pose set that can achieve a near-perfect recognition accuracy. The system consists of a hand pose taxonomy, a pose notation, a machine learning classifier and an algorithm to identify a reliable pose set that can achieve near perfect accuracy levels. We used this system to construct a benchmark hand pose data set containing 2550 static hand pose instances, and show how the algorithm can be used to systematically derive a set of poses that can produce an accuracy of 99% using a Support Vector Machine classifier.

Item Type: Conference paper
Subjects: Computing methodologies > Machine learning > Learning paradigms > Supervised learning
Human-centered computing > Human computer interaction (HCI) > Interaction paradigms > Virtual reality
Human-centered computing > Human computer interaction (HCI) > Interaction techniques > Gestural input
Alternate Locations: https://dl.acm.org/doi/abs/10.1145/3410886.3410909
Date Deposited: 21 Dec 2020 10:48
Last Modified: 21 Dec 2020 10:48
URI: http://pubs.cs.uct.ac.za/id/eprint/1389

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