1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results

• URL: http://arxiv.org/abs/2211.13508v2
• Date: Mon, 28 Nov 2022 18:25:26 GMT
• Title: 1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results
• Authors: Benjamin Kiefer, Matej Kristan, Janez Per\v{s}, Lojze \v{Z}ust, Fabio Poiesi, Fabio Augusto de Alcantara Andrade, Alexandre Bernardino, Matthew Dawkins, Jenni Raitoharju, Yitong Quan, Adem Atmaca, Timon H\"ofer, Qiming Zhang, Yufei Xu, Jing Zhang, Dacheng Tao, Lars Sommer, Raphael Spraul, Hangyue Zhao, Hongpu Zhang, Yanyun Zhao, Jan Lukas Augustin, Eui-ik Jeon, Impyeong Lee, Luca Zedda, Andrea Loddo, Cecilia Di Ruberto, Sagar Verma, Siddharth Gupta, Shishir Muralidhara, Niharika Hegde, Daitao Xing, Nikolaos Evangeliou, Anthony Tzes, Vojt\v{e}ch Bartl, Jakub \v{S}pa\v{n}hel, Adam Herout, Neelanjan Bhowmik, Toby P. Breckon, Shivanand Kundargi, Tejas Anvekar, Chaitra Desai, Ramesh Ashok Tabib, Uma Mudengudi, Arpita Vats, Yang Song, Delong Liu, Yonglin Li, Shuman Li, Chenhao Tan, Long Lan, Vladimir Somers, Christophe De Vleeschouwer, Alexandre Alahi, Hsiang-Wei Huang, Cheng-Yen Yang, Jenq-Neng Hwang, Pyong-Kun Kim, Kwangju Kim, Kyoungoh Lee, Shuai Jiang, Haiwen Li, Zheng Ziqiang, Tuan-Anh Vu, Hai Nguyen-Truong, Sai-Kit Yeung, Zhuang Jia, Sophia Yang, Chih-Chung Hsu, Xiu-Yu Hou, Yu-An Jhang, Simon Yang, Mau-Tsuen Yang
• Abstract summary: This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.
• Score: 152.54137779547068
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The 1$^{\text{st}}$ 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 at https://seadronessee.cs.uni-tuebingen.de/macvi.

Related papers

• The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024 [71.80200746293505]
  2nd Workshop on Maritime Computer Vision (MaCVi) 2024 addresses maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicles (USV)
  arXiv  Detail & Related papers  (2023-11-23T21:01:14Z)
• Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental Comparisons [10.75221614844458]
  We first briefly summarize four challenges for object detection on maritime UAVs, i.e. object feature diversity, device limitation, maritime environment variability, and dataset scarcity. Next, we review the UAV aerial image/video datasets and propose a maritime UAV aerial dataset named MS2ship for ship detection.
  arXiv  Detail & Related papers  (2023-11-14T07:20:38Z)
• Vision-Based Autonomous Navigation for Unmanned Surface Vessel in Extreme Marine Conditions [2.8983738640808645]
  This paper presents an autonomous vision-based navigation framework for tracking target objects in extreme marine conditions. The proposed framework has been thoroughly tested in simulation under extremely reduced visibility due to sandstorms and fog. The results are compared with state-of-the-art de-hazing methods across the benchmarked MBZIRC simulation dataset.
  arXiv  Detail & Related papers  (2023-08-08T14:25:13Z)
• AVisT: A Benchmark for Visual Object Tracking in Adverse Visibility [125.77396380698639]
  AVisT is a benchmark for visual tracking in diverse scenarios with adverse visibility. AVisT comprises 120 challenging sequences with 80k annotated frames, spanning 18 diverse scenarios. We benchmark 17 popular and recent trackers on AVisT with detailed analysis of their tracking performance across attributes.
  arXiv  Detail & Related papers  (2022-08-14T17:49:37Z)
• MODS -- A USV-oriented object detection and obstacle segmentation benchmark [12.356257470551348]
  We introduce a new obstacle detection benchmark MODS, which considers two major perception tasks: maritime object detection and the more general maritime obstacle segmentation. We present a new diverse maritime evaluation dataset containing approximately 81k stereo images synchronized with an on-board IMU, with over 60k objects annotated. We propose a new obstacle segmentation performance evaluation protocol that reflects the detection accuracy in a way meaningful for practical USV navigation.
  arXiv  Detail & Related papers  (2021-05-05T22:40:27Z)
• SeaDronesSee: A Maritime Benchmark for Detecting Humans in Open Water [13.216389226310987]
  This paper introduces a large-scaled visual object detection and tracking benchmark (SeaDronesSee) We collect and annotate over 54,000 frames with 400,000 instances captured from various altitudes and viewing angles ranging from 5 to 260 meters and 0 to 90 degrees. We evaluate multiple state-of-the-art computer vision algorithms on this newly established benchmark serving as baselines.
  arXiv  Detail & Related papers  (2021-05-05T08:18:36Z)
• MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking [72.76685780516371]
  We present MOTChallenge, a benchmark for single-camera Multiple Object Tracking (MOT) The benchmark is focused on multiple people tracking, since pedestrians are by far the most studied object in the tracking community. We provide a categorization of state-of-the-art trackers and a broad error analysis.
  arXiv  Detail & Related papers  (2020-10-15T06:52:16Z)
• Perceiving Traffic from Aerial Images [86.994032967469]
  We propose an object detection method called Butterfly Detector that is tailored to detect objects in aerial images. We evaluate our Butterfly Detector on two publicly available UAV datasets (UAVDT and VisDrone 2019) and show that it outperforms previous state-of-the-art methods while remaining real-time.
  arXiv  Detail & Related papers  (2020-09-16T11:37:43Z)
• MOT20: A benchmark for multi object tracking in crowded scenes [73.92443841487503]
  We present our MOT20benchmark, consisting of 8 new sequences depicting very crowded challenging scenes. The benchmark was presented first at the 4thBMTT MOT Challenge Workshop at the Computer Vision and Pattern Recognition Conference (CVPR)
  arXiv  Detail & Related papers  (2020-03-19T20:08:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.