@inproceedings{9ce9a8b7818c4f0abea5b03ac0e1d398,
title = "Application of GoogLeNet for Ocean-Front Tracking",
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.",
keywords = "deep learning, Ocean-front tracking, sea surface temperature",
author = "Yuting Yang and Lam, {Kin Man} and Eric Rigall and Junyu Dong and Xin Sun and Muwei Jian",
note = "Funding Information: This paper was supported by The Hong Kong Polytechnic University, Hong Kong, under the project SBoS, and by the National Natural Science Foundation of China (No. U1706218, 61971388). Publisher Copyright: {\textcopyright} 2022 SPIE.; 2022 International Workshop on Advanced Imaging Technology, IWAIT 2022 ; Conference date: 04-01-2022 Through 06-01-2022",
year = "2022",
month = apr,
doi = "10.1117/12.2624284",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
pages = "1--5",
editor = "Masayuki Nakajima and Shogo Muramatsu and Jae-Gon Kim and Jing-Ming Guo and Qian Kemao",
booktitle = "International Workshop on Advanced Imaging Technology, IWAIT 2022",
address = "United States",
}