Application of GoogLeNet for Ocean-Front Tracking

Yuting Yang, Kin Man Lam, Eric Rigall, Junyu Dong, Xin Sun, Muwei Jian

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

In recent years, ocean front tracking is of vital importance in ocean-related research, and many algorithms have been proposed to identify ocean fronts. However, all these methods focus on single frame ocean-front classification instead of ocean-front tracking. In this paper, we propose an Ocean-Front Tracking Dataset (OFTraD) and apply GoogLeNet Inception network to track ocean fronts in video sequences. Firstly, the video sequence is split into image blocks, then the image blocks are classified into ocean-front and background by GoogLenet Inception network. Finally, the labeled image blocks are used to reconstruct the video sequence. Experiments show that our algorithm can achieve accurate tracking results.

Original languageEnglish
Title of host publicationInternational Workshop on Advanced Imaging Technology, IWAIT 2022
EditorsMasayuki Nakajima, Shogo Muramatsu, Jae-Gon Kim, Jing-Ming Guo, Qian Kemao
PublisherSPIE
Pages1-5
Number of pages5
ISBN (Electronic)9781510653313
DOIs
Publication statusPublished - Apr 2022
Event2022 International Workshop on Advanced Imaging Technology, IWAIT 2022 - Hong Kong, China
Duration: 4 Jan 20226 Jan 2022

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume12177
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference2022 International Workshop on Advanced Imaging Technology, IWAIT 2022
Country/TerritoryChina
CityHong Kong
Period4/01/226/01/22

Keywords

  • deep learning
  • Ocean-front tracking
  • sea surface temperature

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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