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

Orthogonal Trajectories01:26

Orthogonal Trajectories

304
Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...
304

You might also read

Related Articles

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

Sort by
Same author

Design of a Verification Device of Motor Axle Wheel Load Scales Based on Pump-Controlled Hydraulic Cylinder.

Sensors (Basel, Switzerland)·2025
Same author

Liquid Reservoir Weld Defect Detection Based on Improved YOLOv8s.

Sensors (Basel, Switzerland)·2025
Same author

Aluminum Reservoir Welding Surface Defect Detection Method Based on Three-Dimensional Vision.

Sensors (Basel, Switzerland)·2025
Same author

Six-Dimensional Pose Estimation of Molecular Sieve Drying Package Based on Red Green Blue-Depth Camera.

Sensors (Basel, Switzerland)·2025
Same author

Dress Code Monitoring Method in Industrial Scene Based on Improved YOLOv8n and DeepSORT.

Sensors (Basel, Switzerland)·2024
Same author

Improved Fully Convolutional Siamese Networks for Visual Object Tracking Based on Response Behaviour Analysis.

Sensors (Basel, Switzerland)·2022

Related Experiment Video

Updated: May 5, 2026

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

9.6K

A Visual Trajectory-Based Method for Personnel Behavior Recognition in Industrial Scenarios.

Houquan Wang1, Tao Song1, Zhipeng Xu1

  • 1College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China.

Sensors (Basel, Switzerland)
|October 29, 2025
PubMed
Summary

This study introduces a lightweight framework for accurate industrial personnel behavior recognition, enhancing safety and asset protection. The system improves detection accuracy in challenging environments using advanced AI models.

Keywords:
behavior recognitiondetectionmultiple object trackingperspective transformation

More Related Videos

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

8.1K
Eye Tracking During A Complex Aviation Task For Insights Into Information Processing
07:48

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

Published on: April 4, 2025

1.2K

Related Experiment Videos

Last Updated: May 5, 2026

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

9.6K
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

8.1K
Eye Tracking During A Complex Aviation Task For Insights Into Information Processing
07:48

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

Published on: April 4, 2025

1.2K

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Industrial Safety

Background:

  • Industrial environments present complex challenges for accurate personnel behavior recognition, impacting safety and asset protection.
  • Existing methods often struggle with the accuracy required for real-world industrial applications.

Purpose of the Study:

  • To develop a novel, lightweight framework for robust and efficient real-time personnel behavior recognition in industrial settings.
  • To enhance the accuracy of behavior recognition despite complex environmental conditions.

Main Methods:

  • Enhanced YOLOv8n model with Receptive Field Attention Convolution (RFAConv) and Efficient Multi-scale Attention (EMA) mechanisms.
  • BOT-SORT algorithm for continuous motion trajectory generation and perspective transformation for bird's-eye view creation.
  • Random Forest model for classifying discriminative trajectory features.

Main Results:

  • Achieved a 6.9% increase in AP50 and a 4.2% increase in AP50:95 over the baseline YOLOv8n model.
  • Generated high-fidelity bird's-eye view trajectories through geometric correction.
  • Attained F1-scores exceeding 82% for all behaviors on a proprietary industrial dataset.

Conclusions:

  • The proposed framework offers a robust and efficient solution for real-time personnel behavior recognition in challenging industrial environments.
  • The integration of attention mechanisms and trajectory analysis significantly improves recognition accuracy.
  • Future work will involve exploring advanced algorithms and edge device validation.