Jove
Visualize
Contact Us

Related Experiment Video

Updated: Jun 27, 2026

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats
06:52

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

Published on: April 3, 2026

Human pose tracking in monocular sequence using multilevel structured models.

Mun Wai Lee1, Ramakant Nevatia

  • 1ObjectVideo Inc., Reston, VA 20191, USA. mlee@objectvideo.com

IEEE Transactions on Pattern Analysis and Machine Intelligence
|November 26, 2008
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Structural Classification of Joints01:20

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...

You might also read

Related Articles

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

Sort by
Same author

Ocular Surface Disease and Dry Eye Severity in Glaucoma Patients at Urban Private Eye Care Centres in Malaysia.

Clinical ophthalmology (Auckland, N.Z.)·2024
Same author

Visual outcomes following bilateral implantation of a non-diffractive extended depth of focus toric intraocular lens using a mini-monovision approach.

International ophthalmology·2024
Same author

Approach to a spontaneously ruptured posterior polar cataract.

Journal of cataract and refractive surgery·2023
Same author

Initial Experience With the Wavelight Refractive Suite for Myopic Laser In Situ Keratomileusis.

Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)·2015
Same author

Multiple target tracking by learning-based hierarchical association of detection responses.

IEEE transactions on pattern analysis and machine intelligence·2013
Same author

Improved biocomposite development of poly(vinyl alcohol) and hydroxyapatite for tissue engineering scaffold fabrication using selective laser sintering.

Journal of materials science. Materials in medicine·2007
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

This study presents a novel three-stage method for tracking 3D human body poses in monocular video, effectively handling multiple people and occlusions for realistic applications.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Accurate human body pose tracking in monocular video is crucial for numerous applications.
  • Realistic scenes present challenges like background clutter, appearance variations, and self-occlusion.
  • Tracking multiple individuals introduces further complexity due to inter-occlusion.

Purpose of the Study:

  • To develop a robust method for hierarchical estimation of 3D human body poses from monocular video.
  • To address challenges including automatic initialization, data association, and both self and inter-occlusion.
  • To enable accurate multi-person pose tracking in complex, real-world scenarios.

Main Methods:

  • A three-stage approach with multi-level state representation for hierarchical pose estimation.

Related Experiment Videos

Last Updated: Jun 27, 2026

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats
06:52

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

Published on: April 3, 2026

  • Stage 1: Coarse estimation of human positions and sizes via foreground blob tracking.
  • Stage 2: 2D joint position inference using belief propagation on detected body parts.
  • Stage 3: 3D pose inference using data-driven Markov chain Monte Carlo with 2D belief maps.

    • Main Results:

    • The method successfully tracks multiple individuals in realistic indoor video sequences.
    • Demonstrated capability in handling complex movements such as sitting and turning.
    • Effectively manages self-occlusion and inter-occlusion between multiple people.

    Conclusions:

    • The proposed hierarchical approach provides a robust solution for multi-person 3D pose tracking.
    • The method shows significant improvements in handling challenging real-world conditions.
    • Enables reliable human pose estimation in applications requiring complex motion analysis.