Jove
Visualize
Contact Us

Related Concept Videos

Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

561
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...
561
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

459
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...
459
Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

701
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...
701

You might also read

Related Articles

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

Sort by
Same author

The public mental representations of deepfake technology: An in-depth qualitative exploration through Quora text data analysis.

PloS one·2025
Same author

A Dataset of Annotated Omnidirectional Videos for Distancing Applications.

Journal of imaging·2021
See all related articles
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 Video

Updated: Sep 29, 2025

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

Iterative Multiple Bounding-Box Refinements for Visual Tracking.

Giorgio Cruciata1, Liliana Lo Presti1, Marco La Cascia1

  • 1Dipartimento di Ingegneria, University of Palermo, 90128 Palermo, Italy.

Journal of Imaging
|March 24, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces non-conflicting transformations for improved single-object visual tracking. The novel approach refines bounding boxes iteratively without learning ambiguities, enhancing tracking accuracy and precision.

Keywords:
deep trackingiterative bounding box refinementvisual tracking

More Related Videos

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking
07:21

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

Published on: February 12, 2011

14.5K
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

Related Experiment Videos

Last Updated: Sep 29, 2025

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
Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking
07:21

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

Published on: February 12, 2011

14.5K
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

Area of Science:

  • Computer Vision
  • Machine Learning

Background:

  • Single-object visual tracking refines bounding boxes iteratively using deep models.
  • Current methods apply one transformation per iteration, potentially causing learning ambiguities.

Purpose of the Study:

  • To propose a novel formulation for selecting bounding box refinements in visual tracking.
  • To introduce non-conflicting transformations for ambiguity-free iterative refinement.

Main Methods:

  • Developed a method allowing multiple non-conflicting transformations per iteration.
  • Enabled simultaneous application of refinements without introducing learning ambiguities.

Main Results:

  • Empirical results show improved accuracy and precision in visual tracking.
  • The proposed approach enhances iterative single refinement techniques.

Conclusions:

  • The novel formulation effectively addresses ambiguities in bounding box refinement learning.
  • This method offers a more robust and accurate solution for single-object visual tracking.