Self-learning framework with temporal filtering for robust maritime vessel detection

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

16 Citations (Scopus)

Abstract

With the recent development in ConvNet-based detectors, a successful solution for vessel detection becomes possible. However, it is essential to access a comprehensive annotated training set from different maritime environments. Creating such a dataset is expensive and time consuming. To automate this process, this paper proposes a novel self learning framework which automatically finetunes a generic pre-trained model to any new environment. With this, the framework enables automated labeling of new dataset types. The method first explores the video frames captured from a new target environment to generate the candidate vessel samples. Afterwards, it exploits a temporal filtering concept to verify the correctly generated candidates as new labels for learning, while removing the false positives. Finally, the system updates the vessel model using the provided self-learning dataset. Experimental results on our real-world evaluation dataset show that generalizing a finetuned Single Shot Detector to a new target domain using the proposed self-learning framework increases the average precision and the F1-score by 12% and 5%, respectively. Additionally, the proposed temporal filter reduced the noisy detections in a sensitive setting from 58% to only 5%.

Original languageEnglish
Title of host publicationRepresentations, Analysis and Recognition of Shape and Motion from Imaging Data - 7th International Workshop, RFMI 2017, Revised Selected Papers
EditorsFaouzi Ghorbel, Liming Chen, Boulbaba Ben Amor
Place of PublicationCham
PublisherSpringer
Pages121-135
Number of pages15
ISBN (Electronic)978-3-030-19816-9
ISBN (Print)978-3-030-19815-2
DOIs
Publication statusPublished - 5 May 2019
Event7th International Workshop on Representations, Analysis and Recognition of Shape and Motion from Imaging Data, RFMI 2017 - Savoie, France
Duration: 17 Dec 201720 Dec 2017

Publication series

NameCommunications in Computer and Information Science
Volume842
ISSN (Print)1865-0929

Conference

Conference7th International Workshop on Representations, Analysis and Recognition of Shape and Motion from Imaging Data, RFMI 2017
CountryFrance
CitySavoie
Period17/12/1720/12/17

Keywords

  • Automated dataset creation
  • ConvNet
  • Convolutional networks (CNN)
  • Maritime surveillance
  • Self-learning
  • Vessel detection

Fingerprint Dive into the research topics of 'Self-learning framework with temporal filtering for robust maritime vessel detection'. Together they form a unique fingerprint.

Cite this