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

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

469
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...
469
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

574
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...
574

You might also read

Related Articles

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

Sort by
Same journal

RETRACTION: Clinical Value Study on Contrast-Enhanced Ultrasound Combined with Enhanced CT in Early Diagnosis of Primary Hepatic Carcinoma.

Contrast media & molecular imaging·2026
Same journal

Correction to "Prostate Osteoblast-Like Cells: A Reliable Prognostic Marker of Bone Metastasis in Prostate Cancer Patients".

Contrast media & molecular imaging·2026
Same journal

RETRACTION: Structural and Functional Characterization at the Molecular Level of the MATE Gene Family in Wheat in Silico.

Contrast media & molecular imaging·2025
Same journal

RETRACTION: The Significance of PAX8-PPARγ Expression in Thyroid Cancer and the Application of a PAX8-PPARγ-Targeted Ultrasound Contrast Agent in the Early Diagnosis of Thyroid Cancer.

Contrast media & molecular imaging·2025
Same journal

RETRACTION: COVID-19 Semantic Pneumonia Segmentation and Classification Using Artificial Intelligence.

Contrast media & molecular imaging·2025
Same journal

RETRACTION: Intelligent Algorithm-Based Ultrasound Images in Evaluation of Therapeutic Effects of Radiofrequency Ablation for Liver Tumor and Analysis on Risk Factors of Postoperative Infection.

Contrast media & molecular imaging·2025
See all related articles

Related Experiment Video

Updated: Oct 6, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.1K

Visual Sequence Algorithm for Moving Object Tracking and Detection in Images.

Renzheng Xue1, Ming Liu1, Xiaokun Yu2

  • 1School of Computer and Control Engineering, Qiqihar University, Qiqihar, Heilongjiang 161006, China.

Contrast Media & Molecular Imaging
|January 13, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an improved computer vision algorithm for detecting and tracking moving objects. The enhanced frame difference and mean-shift method offers faster, more accurate results for real-time applications.

More Related Videos

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
05:57

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Published on: April 8, 2019

7.0K
SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware
08:13

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Published on: December 25, 2017

8.3K

Related Experiment Videos

Last Updated: Oct 6, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.1K
Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
05:57

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Published on: April 8, 2019

7.0K
SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware
08:13

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Published on: December 25, 2017

8.3K

Area of Science:

  • Computer Vision
  • Image Processing
  • Algorithm Development

Background:

  • Moving object detection and tracking are crucial in various computer vision applications.
  • Traditional methods often face challenges with accuracy and speed.
  • Optimizing algorithms is essential for real-time performance.

Purpose of the Study:

  • To evaluate and confirm the best algorithm for detecting and tracking moving objects in images.
  • To propose an improved algorithm that enhances detection and tracking capabilities.
  • To assess the effectiveness of the proposed algorithm against existing methods.

Main Methods:

  • An improved frame difference method combined with a single Gaussian background model for target detection.
  • Utilizing the mean-shift algorithm for target tracking and template updating based on a similarity threshold.
  • Dynamically adjusting the search window size and position based on target changes.

Main Results:

  • The proposed algorithm demonstrated improved detection and tracking of moving targets.
  • The Bhattacharyya similarity measure was used to determine template updates and algorithm restarts.
  • Observed dynamic changes in search window position and size correlating with target movement.

Conclusions:

  • The developed algorithm offers a fast and accurate solution for automatic moving object detection and tracking.
  • The integration of improved frame difference and mean-shift methods yields superior performance.
  • This approach provides a robust framework for real-time computer vision tasks.