Accurate Joint Detection from Depth Videos towards Pose Analysis Metadata

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  • Main Title Accurate Joint Detection from Depth Videos towards Pose Analysis


  • Author: Kong, Longbo
    Creator Type: Personal


  • Chair: Yuan, Xiaohui
    Contributor Type: Personal
  • Committee Member: Fu, Song
    Contributor Type: Personal
  • Committee Member: Renka, Robert
    Contributor Type: Personal
  • Committee Member: Takabi, Hassan
    Contributor Type: Personal


  • Name: University of North Texas
    Place of Publication: Denton, Texas


  • Creation: 2018-05


  • English


  • Content Description: Joint detection is vital for characterizing human pose and serves as a foundation for a wide range of computer vision applications such as physical training, health care, entertainment. This dissertation proposed two methods to detect joints in the human body for pose analysis. The first method detects joints by combining body model and automatic feature points detection together. The human body model maps the detected extreme points to the corresponding body parts of the model and detects the position of implicit joints. The dominant joints are detected after implicit joints and extreme points are located by a shortest path based methods. The main contribution of this work is a hybrid framework to detect joints on the human body to achieve robustness to different body shapes or proportions, pose variations and occlusions. Another contribution of this work is the idea of using geodesic features of the human body to build a model for guiding the human pose detection and estimation. The second proposed method detects joints by segmenting human body into parts first and then detect joints by making the detection algorithm focusing on each limb. The advantage of applying body part segmentation first is that the body segmentation method narrows down the searching area for each joint so that the joint detection method can provide more stable and accurate results.


  • Keyword: Joint Detection
  • Keyword: Pose Detection
  • Keyword: Feature Points Extraction
  • Keyword: Human Body Segmentation


  • Name: UNT Theses and Dissertations
    Code: UNTETD


  • Name: UNT Libraries
    Code: UNT


  • Rights Access: public
  • Rights Holder: Kong, Longbo
  • Rights License: copyright
  • Rights Statement: Copyright is held by the author, unless otherwise noted. All rights Reserved.

Resource Type

  • Thesis or Dissertation


  • Text


  • Accession or Local Control No: submission_1028
  • Archival Resource Key: ark:/67531/metadc1157524


  • Degree Name: Doctor of Philosophy
  • Degree Level: Doctoral
  • Academic Department: Department of Computer Science and Engineering
  • College: College of Engineering
  • Degree Discipline: Computer Science and Engineering
  • Degree Publication Type: disse
  • Degree Grantor: University of North Texas


  • Embargo Note: The work will be published after approval.