Pedestrian pose estimation using near set theory

dc.contributor.authorHaider, Muhammad Shangol
dc.contributor.examiningcommitteeHossain, Ekram (Electrical and Computer Engineering) Gilmore, Colin (Electrical and Computer Engineering)en_US
dc.contributor.supervisorPeters, James F. (Electrical and Computer Engineering)en_US
dc.date.accessioned2021-06-11T19:30:14Z
dc.date.available2021-06-11T19:30:14Z
dc.date.copyright2021-06-11
dc.date.issued2021en_US
dc.date.submitted2021-06-11T19:05:33Zen_US
dc.degree.disciplineElectrical and Computer Engineeringen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractDepending on the application type there are numerous stages of an automated computer vision system. Extracting informative foreground from the clutter of background data and identifying patterns are two stages of a typical computer vision system. Work had been done to use near set theory to identify spatially and descriptively near objects within an image. In this thesis, we extended this idea to videos and introduced the notion of temporal proximity for pattern recognition in spatial descriptive temporal domain. Each video is composed of a set of voxels which are the most basic elements of video defined as union of pixel and time stamp data. For extracting useful features, we observe the properties of topological spaces and introduce the concept of optical vortex nerve. Energy, area, 0-simplices (vertices), 1-simplices (edges) and 2-simplices (triangles) are some of the key features observed in this thesis. Finally, I explored pedestrian pose estimation as an application for applying all the concepts developed in this document in order to classify walking and running poses.en_US
dc.description.noteOctober 2021en_US
dc.identifier.urihttp://hdl.handle.net/1993/35700
dc.rightsopen accessen_US
dc.subjectContent-based image retrieval, description, feature vector, probe function,closeness, near set, temporal proximity, maximal nucleus cluster, optical vortex nerve, persistence, proximity, shape, topological shape spaceen_US
dc.titlePedestrian pose estimation using near set theoryen_US
dc.typemaster thesisen_US
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