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Related Experiment Video

Updated: May 8, 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

Monocular 3-D gait tracking in surveillance scenes.

Grégory Rogez, Jonathan Rihan, Jose J Guerrero

    IEEE Transactions on Cybernetics
    |August 20, 2013
    PubMed
    Summary

    This study introduces a novel method for 3D human pose tracking from monocular video, enabling robust gait recognition even with challenging camera angles. The system achieves accurate tracking in complex surveillance scenarios.

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    3D Kinematic Gait Analysis for Preclinical Studies in Rodents
    10:19

    3D Kinematic Gait Analysis for Preclinical Studies in Rodents

    Published on: August 3, 2019

    Related Experiment Videos

    Last Updated: May 8, 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

    3D Kinematic Gait Analysis for Preclinical Studies in Rodents
    10:19

    3D Kinematic Gait Analysis for Preclinical Studies in Rodents

    Published on: August 3, 2019

    Area of Science:

    • Computer Vision
    • Biometrics
    • Human Pose Estimation

    Background:

    • Gait recognition offers noninvasive, remote biometric authentication.
    • Surveillance scenarios present challenges like multiple individuals, camera distance, and angle.
    • Existing systems struggle with view-invariant gait recognition.

    Purpose of the Study:

    • To develop a view-invariant monocular 3D human pose tracking methodology.
    • To enhance gait recognition performance in realistic surveillance settings.
    • To enable accurate 3D pose tracking despite significant camera perspective effects.

    Main Methods:

    • Modeling 3D body poses and camera viewpoints using a low-dimensional manifold.
    • Learning a generative silhouette model from the manifold to training views.
    • Employing recursive Bayesian sampling for joint tracking over ground plane and pose-viewpoint manifold.
    • Utilizing homographic transformation for silhouette projection and matching.

    Main Results:

    • Successful tracking of 3D human walking poses within a 4D state space.
    • Demonstrated significant improvements of homographic alignment over similarity transformation.
    • Provided quantitative pose tracking results on the CAVIAR dataset with high perspective effects.

    Conclusions:

    • The proposed methodology achieves robust 3D human pose tracking in challenging, real-world surveillance conditions.
    • View-invariant tracking is crucial for effective gait recognition in unconstrained environments.
    • The approach offers a promising solution for advanced biometric authentication systems.