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 Experiment Video

Updated: Mar 29, 2026

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

15.4K

Tracking Multiple Video Targets with an Improved GM-PHD Tracker.

Xiaolong Zhou1,2, Hui Yu3, Honghai Liu4

  • 1College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China. zxl@zjut.edu.cn.

Sensors (Basel, Switzerland)
|December 4, 2015
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Sequential changes in calcium transients during M phase regulate cardiomyocyte proliferation.

The Journal of cell biology·2026
Same author

Study on Drying Characteristics of Juvenile Wood of <i>Dalbergia odorifera</i> T.C.Chen.

Materials (Basel, Switzerland)·2026
Same author

Efficacy and effectiveness of robot-assisted therapy for autism spectrum disorder: From lab to reality.

Science robotics·2025
Same author

Efficient speech command recognition leveraging spiking neural networks and progressive time-scaled curriculum distillation.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Text-Derived Relational Graph-Enhanced Network for Skeleton-Based Action Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025
Same author

An Organic Template/Ammonium-Free Strategy to Obtain Ultrastable Y with Intracrystalline Mesoporosity.

ACS applied materials & interfaces·2025
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles
This summary is machine-generated.

This study introduces an improved Gaussian mixture probability hypothesis density (GM-PHD) tracker for multi-target video tracking. The enhanced tracker effectively handles occlusions and close movements using weight penalization and multi-feature fusion.

Area of Science:

  • Computer Vision
  • Robotics
  • Signal Processing

Background:

  • Multi-target tracking is crucial for vision-based robotics.
  • Existing trackers struggle with noisy data, occlusions, and targets in close proximity.

Purpose of the Study:

  • To develop an improved Gaussian mixture probability hypothesis density (GM-PHD) tracker.
  • To enhance accuracy and effectiveness in tracking multiple moving targets from video data.

Main Methods:

  • Incorporated an entropy-based birth intensity estimation to reduce false positives.
  • Developed a weight-penalized method with multi-feature fusion (spatial-color, HOG, area) for close targets.
  • Utilized a game-theoretical approach for occluded targets.

Main Results:

Keywords:
multi-feature fusionprobability hypothesis densityrobot visionvideo targets trackingweight penalization

More Related Videos

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies
07:34

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies

Published on: November 7, 2025

466
Video Tracking Protocol to Screen Deterrent Chemistries for Honey Bees
10:19

Video Tracking Protocol to Screen Deterrent Chemistries for Honey Bees

Published on: June 12, 2017

7.1K

Related Experiment Videos

Last Updated: Mar 29, 2026

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

15.4K
Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies
07:34

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies

Published on: November 7, 2025

466
Video Tracking Protocol to Screen Deterrent Chemistries for Honey Bees
10:19

Video Tracking Protocol to Screen Deterrent Chemistries for Honey Bees

Published on: June 12, 2017

7.1K
  • The proposed method effectively eliminates false positives from noisy video.
  • Accurate tracking of targets in close movement is achieved through weight penalization and feature fusion.
  • Superior performance demonstrated over state-of-the-art trackers in various video scenarios.

Conclusions:

  • The improved GM-PHD tracker with weight penalization offers robust multi-target tracking.
  • The method shows significant improvements in handling challenging scenarios like occlusions and close formations.
  • Validated effectiveness through extensive experiments on diverse video data.