Application of GoogLeNet for ocean-front tracking

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

doi:10.1117/12.2624284

30 April 2022 Application of GoogLeNet for ocean-front tracking

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

Author Affiliations +

Proceedings Volume 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022; 121770W (2022) https://doi.org/10.1117/12.2624284
Event: International Workshop on Advanced Imaging Technology 2022 (IWAIT 2022), 2022, Hong Kong, China

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.

Citation Download Citation

Yuting Yang, Kin-Man Lam, Eric Rigall, Junyu Dong, Xin Sun, and Muwei Jian "Application of GoogLeNet for ocean-front tracking", Proc. SPIE 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022, 121770W (30 April 2022); https://doi.org/10.1117/12.2624284

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