Size-invariant detection of marine vessels from visual time series

Tunai Porto Marques, Alexandra Branzan Albu, Patrick O'Hara, Norma Serra, Ben Morrow, Lauren McWhinnie, Rosaline Canessa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)


Marine vessel traffic is one of the main sources of negative anthropogenic impact upon marine environments. The automatic identification of boats in monitoring images facilitates conservation, research and patrolling efforts. However, the diverse sizes of vessels, the highly dynamic water surface and weather-related visibility issues significantly hinder this task. While recent deep learning (DL)-based object detectors identify well medium- and large-sized boats, smaller vessels, often responsible for substantial disturbance to sensitive marine life, are typically not detected. We propose a detection approach that combines state-of-the-art object detectors and a novel Detector of Small Marine Vessels (DSMV) to identify boats of any size. The DSMV uses a short time series of images and a novel bi-directional Gaussian Mixture technique to determine motion in combination with context-based filtering and a DL-based image classifier. Experimental results obtained on our novel datasets of images containing boats of various sizes show that the proposed approach comfortably outperforms five popular state-of-the-art object detectors. Code and datasets available at

Original languageEnglish
Title of host publication2021 IEEE Winter Conference on Applications of Computer Vision (WACV)
Number of pages11
ISBN (Electronic)9780738142661
Publication statusPublished - 14 Jun 2021
Event2021 IEEE Winter Conference on Applications of Computer Vision - Virtual, Online, United States
Duration: 5 Jan 20219 Jan 2021


Conference2021 IEEE Winter Conference on Applications of Computer Vision
Abbreviated titleWACV 2021
Country/TerritoryUnited States
CityVirtual, Online


  • Marine Vessels
  • Image analysis (IA)

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications


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