ROS Theses Repository

View Item 
  •   ROS Home
  • Engineering & Physical Sciences
  • Doctoral Theses (Engineering & Physical Sciences)
  • View Item
  •   ROS Home
  • Engineering & Physical Sciences
  • Doctoral Theses (Engineering & Physical Sciences)
  • View Item
  •   ROS Home
  • Engineering & Physical Sciences
  • Doctoral Theses (Engineering & Physical Sciences)
  • View Item
  • Admin
JavaScript is disabled for your browser. Some features of this site may not work without it.

Covariate factor mitigation techniques for robust gait recognition

View/Open
WhytockT_0515_eps.pdf (50.11Mb)
Date
2015-05
Author
Whytock, Tenika P.
Metadata
Show full item record
Abstract
The human gait is a discriminative feature capable of recognising a person by their unique walking manner. Currently gait recognition is based on videos captured in a controlled environment. These videos contain challenges, termed covariate factors, which affect the natural appearance and motion of gait, e.g. carrying a bag, clothing, shoe type and time. However gait recognition has yet to achieve robustness to these covariate factors. To achieve enhanced robustness capabilities, it is essential to address the existing gait recognition limitations. Specifically, this thesis develops an understanding of how covariate factors behave while a person is in motion and the impact covariate factors have on the natural appearance and motion of gait. Enhanced robustness is achieved by producing a combination of novel gait representations and novel covariate factor detection and removal procedures. Having addressed the limitations regarding covariate factors, this thesis achieves the goal of robust gait recognition. Using a skeleton representation of the human figure, the Skeleton Variance Image condenses a skeleton sequence into a single compact 2D gait representation to express the natural gait motion. In addition, a covariate factor detection and removal module is used to maximise the mitigation of covariate factor effects. By establishing the average pixel distribution within training (covariate factor free) representations, a comparison against test (covariate factor) representations achieves effective covariate factor detection. The corresponding difference can effectively remove covariate factors which occur at the boundary of, and hidden within, the human figure.
URI
http://hdl.handle.net/10399/2943
Collections
  • Doctoral Theses (Engineering & Physical Sciences)

Browse

All of ROSCommunities & CollectionsBy Issue DateAuthorsTitlesThis CollectionBy Issue DateAuthorsTitles

ROS Administrator

LoginRegister
©Heriot-Watt University, Edinburgh, Scotland, UK EH14 4AS.

Maintained by the Library
Tel: +44 (0)131 451 3577
Library Email: libhelp@hw.ac.uk
ROS Email: open.access@hw.ac.uk

Scottish registered charity number: SC000278

  • About
  • Copyright
  • Accessibility
  • Policies
  • Privacy & Cookies
  • Feedback
AboutCopyright
AccessibilityPolicies
Privacy & Cookies
Feedback
 
©Heriot-Watt University, Edinburgh, Scotland, UK EH14 4AS.

Maintained by the Library
Tel: +44 (0)131 451 3577
Library Email: libhelp@hw.ac.uk
ROS Email: open.access@hw.ac.uk

Scottish registered charity number: SC000278

  • About
  • Copyright
  • Accessibility
  • Policies
  • Privacy & Cookies
  • Feedback
AboutCopyright
AccessibilityPolicies
Privacy & Cookies
Feedback