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

Force Classification01:22

Force Classification

1.8K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.8K
Classification of Systems-II01:31

Classification of Systems-II

251
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
251
Aggregates Classification01:29

Aggregates Classification

400
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
400
Methods of Classification and Identification01:28

Methods of Classification and Identification

290
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
290
Classification of Systems-I01:26

Classification of Systems-I

346
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
346
Classification of Signals01:30

Classification of Signals

978
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
978

You might also read

Related Articles

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

Sort by
Same author

Speeding Up the Discovery of Optimal Feature Combinations for Omics Data Based on Pseudo-Kernel Function.

Research square·2026
Same author

Adaptation Outcomes of a CAPABLE-Family Open-Label Pilot Study for Community-Dwelling Older Adults With Mild Cognitive Impairment/Early Dementia and Disability.

Journal of applied gerontology : the official journal of the Southern Gerontological Society·2026
Same author

Preparation and Characterization of Icariin-Loaded Bioactive Glass/Sodium Alginate Thermosensitive Composite Gel.

ACS applied bio materials·2026
Same author

800 million years of co-evolution in the green plant lineage - the case of LEUNIG and SEUSS transcriptional co-regulators.

Molecular biology and evolution·2026
Same author

ScopeViewer: A Browser-Based Solution for Visualizing Large Biological Images.

GigaScience·2026
Same author

Spatial Gene Set Enrichment Analysis with Applications to Spatially Resolved Transcriptomic Data.

bioRxiv : the preprint server for biology·2026
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

Related Experiment Video

Updated: Sep 29, 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

Extended Object Tracking with Embedded Classification.

Wen Cao1, Qiwei Li2

  • 1School of Electronics and Control Engineering, Chang'an University, Xi'an 710064, China.

Sensors (Basel, Switzerland)
|March 26, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new extended object tracking (EOT) method that integrates classification. The novel approach enhances tracking performance by embedding classification, addressing limitations of traditional EOT systems.

Keywords:
extended object trackinghard classificationrandom matrixsequential probability ratio test (SPRT)soft classification

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

665
Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish
10:56

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Published on: March 6, 2014

12.7K

Related Experiment Videos

Last Updated: Sep 29, 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
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

665
Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish
10:56

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Published on: March 6, 2014

12.7K

Area of Science:

  • Computer Vision
  • Machine Learning
  • Robotics

Background:

  • Traditional extended object tracking (EOT) methods often neglect object classification, limiting their applicability in complex scenarios.
  • Integrating classification into EOT can significantly improve tracking accuracy and provide richer scene understanding.

Purpose of the Study:

  • To develop a systematic Extended Object Tracking (EOT) method with embedded classification.
  • To enhance practical EOT problem-solving by incorporating classification as a subproblem.
  • To improve overall tracking performance through classification assistance.

Main Methods:

  • Formulation of the EOT problem with embedded classification using kinematic and attribute models.
  • Development of a random-matrix-based, multiple model EOT method.
  • Implementation of two classification strategies: soft classification and hard classification.
  • Exploration of a sequential probability ratio test for hard classification in EOT.

Main Results:

  • The proposed EOT method with embedded classification demonstrates superior tracking performance compared to traditional methods.
  • Simulation results validate the effectiveness of both soft and hard classification integration.
  • The method successfully meets practical requirements for classification in EOT tasks.

Conclusions:

  • Embedding classification within EOT is a viable and effective strategy.
  • The proposed method offers improved tracking accuracy and broader applicability for extended objects.
  • Classification-assisted tracking provides significant benefits for complex EOT scenarios.