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En jämförelse av inlärningsbaserade lösningar för mänsklig positionsuppskattning i 3D
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
2019 (Swedish)Independent thesis Basic level (university diploma), 10 credits / 15 HE creditsStudent thesisAlternative title
A comparison of learning-based solutions for 3D human pose estimation (English)
Abstract [sv]

Inom områden som idrottsvetenskap och underhållning kan det finnas behov av att analysera en människas kroppsposition i 3D. Dessa behov kan innefatta att analysera en golfsving eller att möjliggöra mänsklig interaktion med spel. För att tillförlitligt uppskatta kroppspositioner krävs det idag specialiserad hårdvara som ofta är dyr och svårtillgänglig. På senare tid har det även tillkommit inlärningsbaserade lösningar som kan utföra samma uppskattning på vanliga bilder. Syftet med arbetet har varit att identifiera och jämföra populära inlärningsbaserade lösningar samt undersöka om någon av dessa presterar i paritet med en etablerad hårdvarubaserad lösning. För detta har testverktyg utvecklats, positionsuppskattningar genomförts och resul- tatdata för samtliga tester analyserats. Resultatet har visat att lösningarna inte pre- sterar likvärdigt med Kinect och att de i nuläget inte är tillräckligt välutvecklade för att användas som substitut för specialiserad hårdvara.

Abstract [en]

In fields such as sports science and entertainment, there’s occasionally a need to an- alyze a person's body pose in 3D. These needs may include analyzing a golf swing or enabling human interaction with games. Today, in order to reliably perform a human pose estimation, specialized hardware is usually required, which is often expensive and difficult to access. In recent years, multiple learning-based solutions have been developed that can perform the same kind of estimation on ordinary images. The purpose of this report has been to identify and compare popular learning-based so- lutions and to investigate whether any of these perform on par with an established hardware-based solution. To accomplish this, tools for testing have been developed, pose estimations have been conducted and result data for each test have been ana- lyzed. The result has shown that the solutions do not perform on par with Kinect and that they are currently not sufficiently well-developed to be used as a substitute for specialized hardware.

Place, publisher, year, edition, pages
2019. , p. 44
Series
TRITA-CBH-GRU ; 2019:026
Keywords [en]
pose estimation, body pose, 3D, RGB image, Microsoft Kinect
Keywords [sv]
positionsuppskattning, kroppsposition, 3D, RGB-bild, Microsoft Kinect
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-252920OAI: oai:DiVA.org:kth-252920DiVA, id: diva2:1322863
Educational program
Bachelor of Science in Engineering - Computer Engineering
Supervisors
Examiners
Available from: 2019-06-11 Created: 2019-06-11 Last updated: 2019-06-11Bibliographically approved

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