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

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.4K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
6.4K

You might also read

Related Articles

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

Sort by
Same author

Information sharing through digitalisation in decentralised supply chains.

Annals of operations research·2022
Same author

Marine Robotics Competitions: a Survey.

Current robotics reports·2022
Same author

COVID-19 impact on global maritime mobility.

Scientific reports·2021
Same author

Choice of three different intramedullary nails in the treatment of trochanteric fractures: Outcome, analysis and consideration in midterm.

Injury·2019
Same author

The effectiveness of long-term course of Sterimar Mn nasal spray for treatment of the recurrence rates of acute allergic rhinitis in patients with chronic allergic rhinitis.

Drug design, development and therapy·2018
Same author

Mission Planning and Decision Support for Underwater Glider Networks: A Sampling on-Demand Approach.

Sensors (Basel, Switzerland)·2015

Related Experiment Video

Updated: Jul 20, 2025

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.5K

Saliency-Aided Online RPCA for Moving Target Detection in Infrared Maritime Scenarios.

Osvaldo Pulpito1,2, Nicola Acito1, Marco Diani3

  • 1Department of Information Engineering, University of Pisa, 56122 Pisa, Italy.

Sensors (Basel, Switzerland)
|July 29, 2023
PubMed
Summary

This study introduces a saliency-aided online moving window RPCA (S-OMW-RPCA) for detecting moving targets in maritime infrared surveillance. The enhanced method improves robustness and accuracy by integrating spatial features, outperforming standard online RPCA.

Keywords:
automatic surveillancedata driveninfrared imagesmachine learningmaritime scenariomoving target detectionnaval targetsreal timerobust principal component analysissaliency

More Related Videos

An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles
09:27

An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles

Published on: August 25, 2020

4.3K
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

571

Related Experiment Videos

Last Updated: Jul 20, 2025

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.5K
An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles
09:27

An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles

Published on: August 25, 2020

4.3K
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

571

Area of Science:

  • Computer Vision
  • Signal Processing

Background:

  • Moving target detection (MTD) is essential for surveillance.
  • Infrared (IR) video analysis in maritime settings presents unique challenges.
  • Robust Principal Component Analysis (RPCA) is effective for background/foreground separation.

Purpose of the Study:

  • To develop a real-time MTD technique for maritime IR surveillance.
  • To address limitations of online RPCA during initialization.
  • To improve MTD performance by incorporating spatial information.

Main Methods:

  • Employed Robust Principal Component Analysis (RPCA) for matrix decomposition.
  • Implemented an online, moving window version of RPCA.
  • Integrated a saliency-based strategy to enhance initialization robustness.
  • Developed the saliency-aided online moving window RPCA (S-OMW-RPCA).

Main Results:

  • The S-OMW-RPCA method demonstrated improved robustness against initialization conditions.
  • The technique effectively utilizes both temporal (RPCA) and spatial (saliency) features.
  • Performance comparison showed S-OMW-RPCA superior to standard online RPCA in precision, recall, and execution time.

Conclusions:

  • The proposed S-OMW-RPCA offers an effective and robust solution for real-time MTD in maritime IR surveillance.
  • Integrating saliency information significantly enhances the performance of online RPCA.
  • The method provides a valuable advancement for automated maritime security systems.