Title page for ETD etd-07222010-143454

Type of Document Dissertation
Author Smith, Benjamin A
Author's Email Address smithb@vt.edu
URN etd-07222010-143454
Title Model Free Human Pose Estimation with Application to the Classification of Abnormal Human Movement and the Detection of Hidden Loads
Degree PhD
Department Mechanical Engineering
Advisory Committee
Advisor Name Title
Roach, John W. Committee Chair
Hong, Dennis W. Committee Member
Johnson, Martin E. Committee Member
Lockhart, Thurmon E. Committee Member
Ramu, Krishnan Committee Member
  • Gait analysis
  • pattern recognition
  • hidden load detection
  • computer vision
  • surveillance detection
Date of Defense 2010-07-21
Availability unrestricted
The extraction and analysis of human gait characteristics using image sequences are an important area of research. Recently, the focus of this research area has turned to computer vision as an unobtrusive way to analyze human motions. The applications for such a system are wide ranging in many disciplines. For example, it has been shown that visual systems can be used to identify people by their gait, estimate a subject’s kinematic configuration and identify abnormal motion. The focus of this thesis is a system that accurately classifies observed motions without the use of an explicit spatial or temporal model. The visual detection of hidden loads through passive visual analysis of gait is presented as a test of the system.

The major contributions of this thesis are in two areas. The first is a neural network based scheme that classifies walking styles based on simple image metrics obtained from a single, monocular gray scale image sequence. The powerful neural network classifier utilized in this system provides an efficient, robust and highly accurate classification using these image metrics. This eliminates the need for more complex and difficult to obtain measures that are required by many of the currently human visual analysis systems. This system uses computer vision and pattern recognition techniques combined with physiological knowledge of human gait to estimate an observed subject’s hip angle. The hip angle is then used to calculate a normality index of the gait. The hip angle estimate and normality index are then used as inputs to a neural network. It is shown through experiment that this system provides an accurate classification of four different walking styles observed by a single camera. Secondly, a computer vision based approach is presented that provides an accurate pose estimate without the use of an explicit spatial or temporal model. A hybrid fuzzy neural network is used to assign contour points of a silhouette to kinematically relevant groups. These labeled points are used to estimate the joint locations of the subject. The joint angles are shown to be good estimates as compared to ground truth angles provided by a motion capture system. The effectiveness of the system to distinguish between subtle gait differences is demonstrated by detecting the presence of hidden loads when carried by walking people.

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