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Inferring Human Pose and Motion from Images

Lu, Yifan

Description

As optical gesture recognition technology advances, touchless human computer interfaces of the future will soon become a reality. One particular technology, markerless motion capture, has gained a large amount of attention, with widespread application in diverse disciplines, including medical science, sports analysis, advanced user interfaces, and virtual arts. However, the complexity of human anatomy makes markerless motion capture a non-trivial problem: I) parameterised pose configuration...[Show more]

dc.contributor.authorLu, Yifan
dc.date.accessioned2011-08-08T06:59:57Z
dc.date.available2011-08-08T06:59:57Z
dc.identifier.otherb25317635
dc.identifier.urihttp://hdl.handle.net/1885/8222
dc.description.abstractAs optical gesture recognition technology advances, touchless human computer interfaces of the future will soon become a reality. One particular technology, markerless motion capture, has gained a large amount of attention, with widespread application in diverse disciplines, including medical science, sports analysis, advanced user interfaces, and virtual arts. However, the complexity of human anatomy makes markerless motion capture a non-trivial problem: I) parameterised pose configuration exhibits high dimensionality, and II) there is considerable ambiguity in surjective inverse mapping from observation to pose configuration spaces with a limited number of camera views. These factors together lead to multimodality in high dimensional space, making markerless motion capture an ill-posed problem. This study challenges these difficulties by introducing a new framework. It begins with automatically modelling specific subject template models and calibrating posture at the initial stage. Subsequent tracking is accomplished by embedding naturally-inspired global optimisation into the sequential Bayesian filtering framework. Tracking is enhanced by several robust evaluation improvements. Sparsity of images is managed by compressive evaluation, further accelerating computational efficiency in high dimensional space.
dc.language.isoen_AU
dc.subjectHuman Motion Tracking, Markerless Motion Capture
dc.titleInferring Human Pose and Motion from Images
dc.typeThesis (PhD)
local.contributor.supervisorHartley, Richard
dcterms.valid2011
local.description.notesSupervisor - Dr Richard Hartley
local.description.refereedYes
local.type.degreeDoctor of Philosophy (PhD)
dc.date.issued2011
local.contributor.affiliationCollege of Engineering and Computer Science
local.identifier.doi10.25911/5d7a2682becf6
local.mintdoimint
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