A multiple optical tracking based approach for enhancing hand-based interaction in virtual reality simulations
type: CreativeWork
description: Research exploring natural virtual reality interaction has seen significant success in optical tracker-based approaches, enabling users to freely interact using their hands. Optical based trackers can provide users with real-time, high-fidelity virtual hand representations for natural interaction and an immersive experience. However, work in this area has identified four issues: occlusion, field-of-view, stability and accuracy. To overcome the four key issues, researchers have investigated approaches such as using multiple sensors. Research has shown multi-sensor-based approaches to be effective in improving recognition accuracy. However, such approaches typically use statically positioned sensors, which introduce body occlusion issues that make tracking hands challenging. Machine learning approaches have also been explored to improve gesture recognition. However, such approaches typically require a pre-set gesture vocabulary limiting user actions with larger vocabularies hindering real-time performance. This thesis presents an optical hand-based interaction system that comprises two Leap Motion sensors mounted onto a VR headset at different orientations. Novel approaches to the aggregation and validation of sensor data are presented. A machine learning sub-system is developed to validate hand data received by the sensors. Occlusion detection, stability detection, inferred hands and a hand interpolation sub-system are also developed to ensure that valid hand representations are always shown to the user. In addition, a mesh conformation sub-system ensures 3D objects are appropriately held in a user’s virtual hand. The presented system addresses the four key issues of optical sessions to provide a smooth and consistent user experience. The MOT system is evaluated against traditional interaction approaches; gloves, motion controllers and a single front-facing sensor configuration. The comparative sensor evaluation analysed the validity and availability of tracking data…
abstract: Research exploring natural virtual reality interaction has seen significant success in optical tracker-based approaches, enabling users to freely interact using their hands. Optical based trackers can provide users with real-time, high-fidelity virtual hand representations for natural interaction and an immersive experience. However, work in this area has identified four issues: occlusion, field-of-view, stability and accuracy. To overcome the four key issues, researchers have investigated approaches such as using multiple sensors. Research has shown multi-sensor-based approaches to be effective in improving recognition accuracy. However, such approaches typically use statically positioned sensors, which introduce body occlusion issues that make tracking hands challenging. Machine learning approaches have also been explored to improve gesture recognition. However, such approaches typically require a pre-set gesture vocabulary limiting user actions with larger vocabularies hindering real-time performance. This thesis presents an optical hand-based interaction system that comprises two Leap Motion sensors mounted onto a VR headset at different orientations. Novel approaches to the aggregation and validation of sensor data are presented. A machine learning sub-system is developed to validate hand data received by the sensors. Occlusion detection, stability detection, inferred hands and a hand interpolation sub-system are also developed to ensure that valid hand representations are always shown to the user. In addition, a mesh conformation sub-system ensures 3D objects are appropriately held in a user’s virtual hand. The presented system addresses the four key issues of optical sessions to provide a smooth and consistent user experience. The MOT system is evaluated against traditional interaction approaches; gloves, motion controllers and a single front-facing sensor configuration. The comparative sensor evaluation analysed the validity and availability of tracking data…
author: Worrallo, Adam Grant
keywords: gestures, virtual reality, hand interaction, optical tracking, leap motion, natural interaction, occlusion, machine learning, sensor function, realistic
license: https://wlv.dspace7.openrepository.com/bitstreams/c6bb5619-45b5-4e2b-9939-da14deaed5f9/download
date published: 2020-09
date modified: 2026-07-24T06:10:39Z
provider: University of Wolverhampton
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Wolverhampton