Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Methods of Classification and Identification

Methods of Classification and Identification

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...

You might also read

Related Articles

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

Sort by

Same author

eWaSR-An Embedded-Compute-Ready Maritime Obstacle Detection Network.

Sensors (Basel, Switzerland)·2023

Same author

Joint Calibration of a Multimodal Sensor System for Autonomous Vehicles.

Sensors (Basel, Switzerland)·2023

Same author

Learning with Weak Annotations for Robust Maritime Obstacle Detection.

Sensors (Basel, Switzerland)·2022

Same author

A Discriminative Single-Shot Segmentation Network for Visual Object Tracking.

IEEE transactions on pattern analysis and machine intelligence·2021

Same author

WaSR-A Water Segmentation and Refinement Maritime Obstacle Detection Network.

IEEE transactions on cybernetics·2021

Same author

Cognitive Relevance Transform for Population Re-Targeting.

Sensors (Basel, Switzerland)·2020

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

Search research articles

Related Experiment Video

Updated: May 15, 2026

Harmonic Radar Tags for Insect Tracking: Lightweight, Low-cost, and Accessible

Harmonic Radar Tags for Insect Tracking: Lightweight, Low-cost, and Accessible

Published on: May 13, 2025

Tracking by identification using computer vision and radio.

Rok Mandeljc¹, Stanislav Kovačič, Matej Kristan

¹Machine Vision Laboratory, Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, SI-1000 Ljubljana, Slovenia. rok.mandeljc@fe.uni-lj.si

Sensors (Basel, Switzerland)

|December 25, 2012

Summary

This summary is machine-generated.

This study introduces a novel system fusing computer vision and radio localization for accurate multi-person tracking. This fusion prevents identity errors, outperforming individual methods in complex indoor environments.

More Related Videos

Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees

Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees

Published on: November 15, 2014

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

Related Experiment Videos

Last Updated: May 15, 2026

Harmonic Radar Tags for Insect Tracking: Lightweight, Low-cost, and Accessible

Harmonic Radar Tags for Insect Tracking: Lightweight, Low-cost, and Accessible

Published on: May 13, 2025

Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees

Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees

Published on: November 15, 2014

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

Area of Science:

Computer Vision
Robotics
Sensor Fusion

Background:

Accurate multi-person tracking is crucial for applications like surveillance and human-robot interaction.
Existing methods often struggle with identity switches and localization accuracy in cluttered environments.

Purpose of the Study:

To develop and evaluate a novel system for multi-person detection, localization, and tracking.
To fuse multi-view computer vision with radio-based localization for robust performance.
To achieve tracking by identification, preventing identity switch propagation.

Main Methods:

A hybrid system combining multi-view computer vision with a radio-based localization system.
Development of a comprehensive evaluation methodology for person localization in a world coordinate system.
Experimental validation on a challenging indoor dataset with multiple people in a cluttered room.

Main Results:

The proposed fusion system significantly outperforms individual computer vision and radio-based components.
Achieved superior localization accuracy compared to the radio-based system alone.
Successfully prevented identity switch propagation inherent in pure computer vision tracking.

Conclusions:

The fusion of multi-view computer vision and radio-based localization offers a robust solution for multi-person tracking.
This hybrid approach overcomes limitations of individual sensor modalities, enhancing both accuracy and identity preservation.
The developed evaluation methodology provides a standardized approach for assessing person localization systems.