Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

A Bayesian framework for extracting human gait using strong prior knowledge.

Ziheng Zhou1, Adam Prügel-Bennett, Robert I Damper

  • 1Information: Signals, Images, Systems Research Group, School of Electronics and Computer Science, University of Southampton, Highfield, UK. zz02r@ecs.soton.ac.uk

IEEE Transactions on Pattern Analysis and Machine Intelligence
|October 27, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

ENPP1 blockade with a humanized monoclonal antibody enhances renal repair after acute kidney injury.

Cell stem cell·2026
Same author

A pilot study of Galectin-3 targeting in chronic kidney fibrosis and kidney function decline.

BMC nephrology·2026
Same author

Dietary thyme essential oil supplementation improves production performance, egg quality, antioxidant status, and intestinal health in late-phase laying hens.

Poultry science·2026
Same author

Synergistic magnetic and electric activation of ozone for the efficient treatment of high-salinity dyeing wastewater over a wide pH range.

Environmental technology·2026
Same author

Small nucleolar RNA Snora61 drives self-renewal of intestinal stem cells via initiation of Lgr5 transcription.

Nature communications·2026
Same author

Penny-Wise and Pound-Foolish in AI-Generated Image Detection.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Task-KV: Task-aware KV Cache Optimization via Semantic Differentiation of Attention Heads.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Achieving Text-based Person Retrieval with Any Granularity.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

This study introduces a Bayesian framework to extract human gait from monocular video, even in complex environments. The method uses an articulated model and hidden Markov models to improve accuracy on noisy, real-world footage.

Area of Science:

  • Computer Vision
  • Biomechanical Engineering
  • Machine Learning

Background:

  • Extracting full-body human motion from monocular video in complex environments is challenging.
  • Existing methods struggle with noisy data and occlusions, limiting gait analysis.
  • A balance between prior knowledge and data-driven learning is crucial for robust gait extraction.

Purpose of the Study:

  • To develop a consistent Bayesian framework for robust human gait extraction from monocular video.
  • To integrate strong prior knowledge using an articulated model with learnable parameters.
  • To improve gait analysis accuracy in complex, real-world conditions with occlusions and noise.

Main Methods:

  • Proposed a Bayesian framework incorporating an articulated human model with static and dynamic parameters.

Related Experiment Videos

  • Learned model parameter statistics from high-quality indoor data to bootstrap outdoor analysis.
  • Utilized a hidden Markov model (HMM) for automatic detection of walking cycle phases.
  • Demonstrated on silhouettes from fronto-parallel sequences under indoor and outdoor conditions, including added noise and occlusions.

    • Main Results:

    • Achieved accurate gait extraction from significantly poorer quality image sequences compared to previous methods.
    • Successfully handled occlusions from clothing (skirts, trench coats) and accessories (rucksacks).
    • Quantified results using chamfer distance and average pixel error, showing improved performance.
    • Demonstrated enhanced person identification by gait compared to a baseline recognition algorithm.

    Conclusions:

    • The proposed Bayesian framework effectively integrates prior knowledge for robust human gait extraction.
    • The method significantly improves the feasibility of analyzing gait from low-quality and cluttered video data.
    • This approach advances the potential for reliable human motion analysis in unconstrained environments.