Jove
Visualize
Contact Us

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

Revocable Signature Scheme with Implicit and Explicit Certificates.

Entropy (Basel, Switzerland)·2023
Same author

Vessel identification based on automatic hull inscriptions recognition.

PloS one·2022
See all related articles
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 Experiment Video

Updated: Jan 2, 2026

Stepwise Cell Seeding on Tessellated Scaffolds to Study Sprouting Blood Vessels
07:49

Stepwise Cell Seeding on Tessellated Scaffolds to Study Sprouting Blood Vessels

Published on: January 14, 2021

3.9K

Vessel Detection and Tracking Method Based on Video Surveillance.

Natalia Wawrzyniak1, Tomasz Hyla2, Adrian Popik3

  • 1Faculty of Navigation, Maritime University of Szczecin, 70-500 Szczecin, Poland.

Sensors (Basel, Switzerland)
|December 5, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces an automated ship detection and tracking method for existing video systems, improving navigational safety in diverse conditions. The solution shows promise for real-time vessel monitoring in ports and rivers.

Keywords:
inland waterwayreal-time detectionvessel detectionvideo monitoring

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
05:57

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

7.2K
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

13.0K

Related Experiment Videos

Last Updated: Jan 2, 2026

Stepwise Cell Seeding on Tessellated Scaffolds to Study Sprouting Blood Vessels
07:49

Stepwise Cell Seeding on Tessellated Scaffolds to Study Sprouting Blood Vessels

Published on: January 14, 2021

3.9K
Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
05:57

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

7.2K
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

13.0K

Area of Science:

  • Computer Vision
  • Maritime Technology
  • Remote Sensing

Background:

  • Ship detection and tracking are crucial for navigational safety in monitored waterways.
  • Current video monitoring systems often rely on manual operation due to challenges in automated detection.
  • Existing methods struggle with variable environmental conditions and diverse vessel types.

Purpose of the Study:

  • To present an automated method for ship detection and tracking using existing video monitoring systems.
  • To evaluate the proposed method's effectiveness across different camera settings, locations, and environmental conditions.
  • To address the need for a universal automatic detection system for various sailing units.

Main Methods:

  • Utilized video streams from existing monitoring systems for ship detection and tracking.
  • Conducted experiments on three datasets with varying camera characteristics and scene locations.
  • Tested the method under diverse light and weather conditions with a wide range of vessel types.

Main Results:

  • The proposed solution demonstrated usability for ship detection and tracking in ports and rivers.
  • Experiments confirmed effectiveness across different camera setups and environmental variables.
  • Minor challenges were observed with ship wakes and highly unfavorable weather conditions.

Conclusions:

  • The developed method offers a viable approach for automated ship detection and tracking.
  • The solution enhances the utility of existing video monitoring infrastructure for maritime surveillance.
  • Further refinement is needed to address performance limitations in adverse conditions like strong ship wakes.