1st Workshop on Maritime Computer Vision (MaCVi) 2023: challenge results
Publication date
2023-02-07
Document type
Sonstige Konferenzveröffentlichung
Author
Kiefer, Benjamin
Kristan, Matej
Perš, Janez
Žust, Lojze
Poiesi, Fabio
Andrade, Fabio
Bernardino, Alexandre
Dawkins, Matthew
Raitoharju, Jenni
Quan, Yitong
Atmaca, Adem
Höfer, Timon
Zhang, Qiming
Xu, Yufei
Zhang, Jing
Tao, Dacheng
Sommer, Lars
Spraul, Raphael
Zhao, Hangyue
Zhang, Hongpu
Zhao, Yanyun
Jeon, Eui-Ik
Lee, Impyeong
Zedda, Luca
Loddo, Andrea
Ruberto, Cecilia Di
Verma, Sagar
Gupta, Siddharth
Muralidhara, Shishir
Hegde, Niharika
Xing, Daitao
Evangeliou, Nikolaos
Tzes, Anthony
Bartl, Vojtěch
Špaňhel, Jakub
Herout, Adam
Bhowmik, Neelanjan
Breckon, Toby P.
Kundargi, Shivanand
Anvekar, Tejas
Tabib, Ramesh Ashok
Mudengudi, Uma
Vats, Arpita
Song, Yang
Liu, Delong
Li, Yonglin
Li, Shuman
Tan, Chenhao
Lan, Long
Somers, Vladimir
Vleeschouwer, Christophe De
Alahi, Alexandre
Huang, Hsiang-Wei
Yang, Cheng-Yen
Hwang, Jenq–Neng
Kim, Pyong-Kun
Kim, Kwang‐Ju
Lee, Kyoungoh
Jiang, Shuai
Li, Haiwen
Zheng, Ziqiang
Vu, Tuan-Anh
Nguyen-Truong, Hai
Yeung, Sai-Kit
Jia, Zhuang
Yang, Sophia
Hsu, Chih–Chung
Hou, Xiu-Yu
Jhang, Yu-An
Yang, Simon X.
Yang, Mau‐Tsuen
Organisational unit
Conference
Winter Conference on Applications of Computer Vision (WACV 2023) ; Waikoloa, Hawaii ; January 3-7, 2023
Publisher
IEEE
Book title
2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)
First page
265
Last page
302
Part of the university bibliography
✅
Language
English
Abstract
The 1st Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available (https:// seadronessee.cs.uni-tuebingen.de/macvi).
Version
Published version
Access right on openHSU
Metadata only access