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Real-time Software Hand Pose Recognition using Single View Depth Images
Thesis (MEng)--Stellenbosch University, 2014.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2014
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| _version_ | 1867613889524400128 |
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| access_status_str | Open Access |
| author | Alberts, Stefan Francois |
| author2 | Engelbrecht, H. A. |
| author_browse | Alberts, Stefan Francois Engelbrecht, H. A. |
| author_facet | Engelbrecht, H. A. Alberts, Stefan Francois |
| author_sort | Alberts, Stefan Francois |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MEng)--Stellenbosch University, 2014. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/86442 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:43:19.203Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2014 |
| publishDateRange | 2014 |
| publishDateSort | 2014 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/86442 Real-time Software Hand Pose Recognition using Single View Depth Images Alberts, Stefan Francois Engelbrecht, H. A. Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering. Computer vision Hand poses Hand signals Pattern recognition systems Dissertations -- Electrical and electronic engineering Depth sensors Image converters Remote control Motion control devices UCTD Theses -- Electrical and electronic engineering Thesis (MEng)--Stellenbosch University, 2014. ENGLISH ABSTRACT: The fairly recent introduction of low-cost depth sensors such as Microsoft’s Xbox Kinect has encouraged a large amount of research on the use of depth sensors for many common Computer Vision problems. Depth images are advantageous over normal colour images because of how easily objects in a scene can be segregated in real-time. Microsoft used the depth images from the Kinect to successfully separate multiple users and track various larger body joints, but has difficulty tracking smaller joints such as those of the fingers. This is a result of the low resolution and noisy nature of the depth images produced by the Kinect. The objective of this project is to use the depth images produced by the Kinect to remotely track the user’s hands and to recognise the static hand poses in real-time. Such a system would make it possible to control an electronic device from a distance without the use of a remote control. It can be used to control computer systems during computer aided presentations, translate sign language and to provide more hygienic control devices in clean rooms such as operating theatres and electronic laboratories. The proposed system uses the open-source OpenNI framework to retrieve the depth images from the Kinect and to track the user’s hands. Random Decision Forests are trained using computer generated depth images of various hand poses and used to classify the hand regions from a depth image. The region images are processed using a Mean-Shift based joint estimator to find the 3D joint coordinates. These coordinates are finally used to classify the static hand pose using a Support Vector Machine trained using the libSVM library. The system achieves a final accuracy of 95.61% when tested against synthetic data and 81.35% when tested against real world data. AFRIKAANSE OPSOMMING: Die onlangse bekendstelling van lae-koste diepte sensors soos Microsoft se Xbox Kinect het groot belangstelling opgewek in navorsing oor die gebruik van die diepte sensors vir algemene Rekenaarvisie probleme. Diepte beelde maak dit baie eenvoudig om intyds verskillende voorwerpe in ’n toneel van mekaar te skei. Microsoft het diepte beelde van die Kinect gebruik om verskeie persone en hul ledemate suksesvol te volg. Dit kan egter nie kleiner ledemate soos die vingers volg nie as gevolg van die lae resolusie en voorkoms van geraas in die beelde. Die doel van hierdie projek is om die diepte beelde (verkry vanaf die Kinect) te gebruik om intyds ’n gebruiker se hande te volg oor ’n afstand en die statiese handgebare te herken. So ’n stelsel sal dit moontlik maak om elektroniese toestelle oor ’n afstand te kan beheer sonder die gebruik van ’n afstandsbeheerder. Dit kan gebruik word om rekenaarstelsels te beheer gedurende rekenaargesteunde aanbiedings, vir die vertaling van vingertaal en kan ook gebruik word as higiëniese, tasvrye beheer toestelle in skoonkamers soos operasieteaters en elektroniese laboratoriums. Die voorgestelde stelsel maak gebruik van die oopbron OpenNI raamwerk om die diepte beelde vanaf die Kinect te lees en die gebruiker se hande te volg. Lukrake Besluitnemingswoude ("Random Decision Forests") is opgelei met behulp van rekenaar gegenereerde diepte beelde van verskeie handgebare en word gebruik om die verskeie handdele vanaf ’n diepte beeld te klassifiseer. Die 3D koördinate van die hand ledemate word dan verkry deur gebruik te maak van ’n Gemiddelde-Afset gebaseerde ledemaat herkenner. Hierdie koördinate word dan gebruik om die statiese handgebaar te klassifiseer met behulp van ’n Steun-Vektor Masjien ("Support Vector Machine"), opgelei met behulp van die libSVM biblioteek. Die stelsel behaal ’n finale akkuraatheid van 95.61% wanneer dit getoets word teen sintetiese data en 81.35% wanneer getoets word teen werklike data. 2014-04-16T17:29:25Z 2014-04-16T17:29:25Z 2014-04 Thesis http://hdl.handle.net/10019.1/86442 en_ZA Stellenbosch University xxiv, 178 p. : ill. application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Computer vision Hand poses Hand signals Pattern recognition systems Dissertations -- Electrical and electronic engineering Depth sensors Image converters Remote control Motion control devices UCTD Theses -- Electrical and electronic engineering Alberts, Stefan Francois Real-time Software Hand Pose Recognition using Single View Depth Images |
| title | Real-time Software Hand Pose Recognition using Single View Depth Images |
| title_full | Real-time Software Hand Pose Recognition using Single View Depth Images |
| title_fullStr | Real-time Software Hand Pose Recognition using Single View Depth Images |
| title_full_unstemmed | Real-time Software Hand Pose Recognition using Single View Depth Images |
| title_short | Real-time Software Hand Pose Recognition using Single View Depth Images |
| title_sort | real time software hand pose recognition using single view depth images |
| topic | Computer vision Hand poses Hand signals Pattern recognition systems Dissertations -- Electrical and electronic engineering Depth sensors Image converters Remote control Motion control devices UCTD Theses -- Electrical and electronic engineering |
| url | http://hdl.handle.net/10019.1/86442 |
| work_keys_str_mv | AT albertsstefanfrancois realtimesoftwarehandposerecognitionusingsingleviewdepthimages |