Search research articles

Related Concept Videos

Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...

You might also read

Related Articles

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

Sort by

Same author

Pixel-Level RGBT Fusion Tracking via Heterogeneous Multi-Expert Distillation and Decoupled Representation Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same author

Exploration of the Clinical Assessment and Prognostic Value of Society for Cardiovascular Angiography and Intervention Shock Staging in Patients With Acute Myocardial Infarction and Cardiogenic Shock on Veno-arterial Extracorporeal Membrane Oxygenation Support.

Reviews in cardiovascular medicine·2026

Same author

Unveiling the Power of Multi-Modal Template Update in RGBT Tracking.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same author

ECMO management in cardiogenic shock-specialized versus non-cardiogenic shock-specialized centers: a registry-based analysis.

BMC anesthesiology·2025

Same author

Clinical study of area-decreasing technique and conventional post-closure technique for V-A ECMO weaning application: study protocol of a randomised controlled trial.

BMJ open·2025

Same author

Prophylactic VA-ECMO During Complex High-Risk PCI: A Randomized Controlled Trial.

JACC. Advances·2025

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

Search research articles

Related Experiment Video

Updated: Sep 29, 2025

How to Build a Dichoptic Presentation System That Includes an Eye Tracker

How to Build a Dichoptic Presentation System That Includes an Eye Tracker

Published on: September 6, 2017

Duality-Gated Mutual Condition Network for RGBT Tracking.

Andong Lu, Cun Qian, Chenglong Li

IEEE Transactions on Neural Networks and Learning Systems

|March 18, 2022

Summary

This summary is machine-generated.

This study introduces a new network for RGB-Thermal (RGBT) tracking, effectively using noisy data. The duality-gated mutual condition network enhances target representations, improving tracking performance even with low-quality sensors.

More Related Videos

Multi-Fiber Photometry to Record Neural Activity in Freely-Moving Animals

Multi-Fiber Photometry to Record Neural Activity in Freely-Moving Animals

Published on: October 20, 2019

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking

Published on: December 16, 2010

Related Experiment Videos

Last Updated: Sep 29, 2025

How to Build a Dichoptic Presentation System That Includes an Eye Tracker

How to Build a Dichoptic Presentation System That Includes an Eye Tracker

Published on: September 6, 2017

Multi-Fiber Photometry to Record Neural Activity in Freely-Moving Animals

Multi-Fiber Photometry to Record Neural Activity in Freely-Moving Animals

Published on: October 20, 2019

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking

Published on: December 16, 2010

Area of Science:

Computer Vision
Machine Learning
Robotics

Background:

RGB-Thermal (RGBT) tracking often struggles with low-quality sensor data containing noise.
Existing algorithms underutilize discriminative features present in noisy modalities.
Sudden camera motion frequently causes tracking failures in RGBT systems.

Purpose of the Study:

To develop a novel network for RGBT tracking that exploits discriminative features from all modalities, including low-quality ones.
To suppress noise and enhance target representations by leveraging cross-modal information.
To address tracking failures due to sudden camera motion.

Main Methods:

Proposing a duality-gated mutual condition network for RGBT tracking.
Designing a mutual condition module to guide feature learning across modalities.
Integrating a duality-gated mechanism to refine conditions and reduce noise.
Implementing an optical flow-based resampling strategy for sudden camera motion detection and correction.

Main Results:

The proposed network effectively enhances target representations from all modalities, even low-quality ones.
The duality-gated mechanism successfully suppresses noise and improves feature quality.
The resampling strategy mitigates tracking failures caused by sudden camera motion.
Extensive experiments demonstrate superior performance compared to state-of-the-art RGBT tracking methods on benchmark datasets.

Conclusions:

The duality-gated mutual condition network offers a robust solution for RGBT tracking, particularly in challenging conditions with noisy data.
The method effectively balances the exploitation of discriminative features and suppression of noise.
The integrated resampling strategy enhances tracking robustness against abrupt camera movements.