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

You might also read

Related Articles

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

Sort by

Same author

Amodal Segmentation and Trait Extraction of On-Branch Soybean Pods with a Synthetic Dual-Mask Dataset.

Sensors (Basel, Switzerland)·2025

Same author

Driving-Related Cognitive Abilities Prediction Based on Transformer's Multimodal Fusion Framework.

Sensors (Basel, Switzerland)·2025

Same author

EasyDAM_V2: Efficient Data Labeling Method for Multishape, Cross-Species Fruit Detection.

Plant phenomics (Washington, D.C.)·2022

Same author

A Pruning Method for Deep Convolutional Network Based on Heat Map Generation Metrics.

Sensors (Basel, Switzerland)·2022

Same author

Easy domain adaptation method for filling the species gap in deep learning-based fruit detection.

Horticulture research·2021

Same author

Asymmetric Adaptive Fusion in a Two-Stream Network for RGB-D Human Detection.

Sensors (Basel, Switzerland)·2021

Search research articles

Related Experiment Video

Updated: Nov 28, 2025

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

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

Published on: December 15, 2023

Multi-Object Tracking Algorithm for RGB-D Images Based on Asymmetric Dual Siamese Networks.

Wen-Li Zhang¹, Kun Yang¹, Yi-Tao Xin¹

¹Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.

Sensors (Basel, Switzerland)

|December 1, 2020

Summary

This summary is machine-generated.

This study introduces a new Multi-Object Tracking (MOT) algorithm using RGB-D images to improve security systems. The ADSiamMOT-RGBD method effectively reduces trajectory drift and interruptions in crowded indoor environments.

Keywords:

RGB-D asymmetric dual Siamese network multi-object tracking

More Related Videos

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Published on: April 8, 2019

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

Related Experiment Videos

Last Updated: Nov 28, 2025

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

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

Published on: December 15, 2023

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Published on: April 8, 2019

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

Area of Science:

Computer Vision
Artificial Intelligence
Robotics

Background:

Intelligent security systems rely on Multi-Object Tracking (MOT) for indoor safety.
Existing MOT algorithms struggle with trajectory drift and interruptions in crowded scenes.

Purpose of the Study:

To develop an improved MOT algorithm for RGB-D images to address limitations in crowded environments.
To enhance the accuracy and reliability of object tracking in intelligent security systems.

Main Methods:

Proposed Asymmetric Dual Siamese networks for RGB-D Multi-Object Tracking (ADSiamMOT-RGBD).
Combined RGB appearance and depth contour information.
Utilized an attention module to suppress redundant features and a trajectory analysis module for error reduction.

Main Results:

The ADSiamMOT-RGBD algorithm demonstrated superior tracking quality compared to previous methods.
Significant improvements were observed on the MICC, EPFL, and UM datasets.
Reduced trajectory drift and interruptions in crowded scenarios.

Conclusions:

The proposed ADSiamMOT-RGBD algorithm effectively enhances MOT performance in challenging indoor environments.
This method offers a more reliable solution for intelligent security systems.
Future work can explore further optimizations for real-world deployment.