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 Concept Videos

Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

666
Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the...
666
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

549
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
549
Planar Rigid-Body Motion01:22

Planar Rigid-Body Motion

565
Understanding the movement of a rigid body in planar motion involves recognizing that every particle within this body is traversing a path that maintains a consistent distance from a specific plane. This concept is fundamental in the study of physics and mechanical engineering, and it allows us to comprehend better how objects move in space.
Planar motion is typically divided into three distinct categories. The first is rectilinear translation, demonstrated by a subway train that moves along...
565
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

278
Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the...
278
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

456
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
456
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

132
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
132

You might also read

Related Articles

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

Sort by
Same author

Effect of control frequency on closed-loop electrical muscle stimulation for biceps-triceps in healthy participants: a pilot study.

Scientific reports·2026
Same author

Multidetector computed tomography angiography predicts the need for bronchial artery embolization in hemoptysis: A retrospective cohort study of 32 patients.

Respiratory medicine case reports·2025
Same author

High-Speed Multiple Object Tracking Based on Fusion of Intelligent and Real-Time Image Processing.

Sensors (Basel, Switzerland)·2025
Same author

Ultra-low-latency 3D measurement with a 1D approximate de Bruijn pattern.

Optics letters·2025
Same author

Klinefelter syndrome diagnosed at autopsy and small-cell lung carcinoma.

Respiratory medicine case reports·2025
Same author

High-Speed Tracking with Mutual Assistance of Feature Filters and Detectors.

Sensors (Basel, Switzerland)·2023

Related Experiment Video

Updated: Sep 21, 2025

A Protocol for Real-time 3D Single Particle Tracking
10:16

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

15.0K

Real-Time Occlusion-Robust Deformable Linear Object Tracking With Model-Based Gaussian Mixture Model.

Taohan Wang1, Yuji Yamakawa2

  • 1School of Engineering, The University of Tokyo, Tokyo, Japan.

Frontiers in Neurorobotics
|June 1, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm for tracking deformable linear objects (DLOs) using Coherent Point Drift and a finite element method model. The method accurately estimates DLO states even with occlusion.

Keywords:
Coherent Point Drift (CPD)Gaussian mixture model (GMM)deformable linear object (DLO)finite element method (FEM)real-timetracking

More Related Videos

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

1.7K
A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.8K

Related Experiment Videos

Last Updated: Sep 21, 2025

A Protocol for Real-time 3D Single Particle Tracking
10:16

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

15.0K
Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

1.7K
A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.8K

Area of Science:

  • Robotics and Computer Vision
  • Deformable Object Manipulation
  • State Estimation

Background:

  • Tracking deformable linear objects (DLOs) is vital for industrial applications but challenging due to occlusion and complex physical properties.
  • Accurate state estimation of DLOs is crucial for effective manipulation and control.

Purpose of the Study:

  • To develop a novel tracking algorithm for observing and estimating the states of deformable linear objects (DLOs).
  • To address challenges in DLO tracking, particularly in scenarios with heavy occlusion and varying physical properties.

Main Methods:

  • The proposed algorithm integrates Coherent Point Drift (CPD) for point cloud registration with a finite element method (FEM) model to encode physical properties.
  • A Gaussian mixture model with CPD regularization is employed to deform the FEM model, capturing local structure, global topology, and material properties.
  • The method avoids the need for simulation software by directly encoding physical properties into the FEM model.

Main Results:

  • Simulations and real-world experiments on objects like ropes and iron wires demonstrate the algorithm's robustness and accuracy.
  • The tracking method effectively handles occlusion, a significant challenge in deformable object tracking.
  • The FEM model accurately approximates real-world deformation processes without relying on external simulations.

Conclusions:

  • The novel tracking algorithm provides a robust and accurate solution for estimating the states of deformable linear objects (DLOs).
  • The integration of CPD and FEM models offers a powerful approach for handling occlusion and complex physical properties in DLO tracking.
  • This method has significant potential for advancing industrial applications requiring precise manipulation of deformable objects.