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Related Experiment Video

Updated: Jun 19, 2026

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
06:54

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Published on: March 4, 2018

Self-calibrating view-invariant gait biometrics.

Michela Goffredo1, Imed Bouchrika, John N Carter

  • 1Department of Applied Electronics, University Roma Tre, Rome, Italy. mg2@ecs.soton.ac.uk

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|November 4, 2009
PubMed
Summary

This study introduces a novel, self-calibrating gait biometrics system using a single camera for viewpoint-independent human identification. The method achieves 73.6% accuracy across diverse views, ideal for covert surveillance applications.

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Area of Science:

  • Biometrics
  • Computer Vision
  • Pattern Recognition

Background:

  • Gait biometrics offers unobtrusive human identification.
  • Existing methods often require specific camera calibration and controlled walking directions.
  • Viewpoint variations pose a significant challenge for gait recognition systems.

Purpose of the Study:

  • To develop a viewpoint-independent gait biometrics system.
  • To enable accurate human identification using gait, even with varying camera angles and without prior calibration.
  • To assess the system's effectiveness in real-world surveillance scenarios.

Main Methods:

  • A novel self-calibrating formulation for gait analysis.
  • Feature extraction and recognition using dynamic gait characteristics.

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Last Updated: Jun 19, 2026

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
06:54

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Published on: March 4, 2018

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
08:24

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Published on: August 30, 2016

  • Evaluation on SOTON and CASIA-B multiview gait databases.

    • Main Results:

    • Achieved a mean correct classification rate of 73.6% across all views on the CASIA-B database.
    • Demonstrated robustness to variations in walking direction and camera viewpoint.
    • Showcased effective performance without knowledge of camera parameters.

    Conclusions:

    • The proposed self-calibrating gait biometrics method is effective for viewpoint-independent human identification.
    • The system's unobtrusive nature and accuracy make it highly suitable for surveillance applications.
    • Future work could explore further enhancements for even greater accuracy and broader applicability.