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Cited by 253Advanced Neural Network ApplicationsVideo Surveillance and Tracking MethodsVisual Attention and Saliency Detection

SeaDronesSee: A Maritime Benchmark for Detecting Humans in Open Water

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Varga, Leon Amadeus, Kiefer, Benjamin, Messmer, Martin, Zell, Andreas·2022·2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
DOI 10.1109/wacv51458.2022.00374
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Abstract

Unmanned Aerial Vehicles (UAVs) are of crucial importance in search and rescue missions in maritime environments due to their flexible and fast operation capabilities. Modern computer vision algorithms are of great interest in aiding such missions. However, they are dependent on large amounts of real-case training data from UAVs, which is only available for traffic scenarios on land. Moreover, current object detection and tracking data sets only provide limited environmental information or none at all, neglecting a valuable source of information. Therefore, this paper introduces a large-scaled visual object detection and tracking benchmark (SeaDronesSee) aiming to bridge the gap from land-based vision systems to sea-based ones. 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 while providing the respective meta information for altitude, viewing angle and other meta data. We evaluate multiple state-of-the-art computer vision algorithms on this newly established benchmark serving as baselines. We provide an evaluation server where researchers can upload their prediction and compare their results on a central leaderboard1.

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  • The SafeSea dataset was created by transforming 300 calm ocean background images originally sourced from the SeaDroneSee dataset.

    “The SafeSea dataset is created using the SafeSea method, involving the transformation of 300 calm ocean background images originally sourced from the 'SeaDroneSee' dataset [34].”

    From SafeSea: Synthetic Data Generation for Adverse & Low Probability Maritime Conditions (2023) · cites this paper as [34]

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Cited by

Sorted by citations

Authors
Varga, L. A., Kiefer, B., Messmer, M., & Zell, A.
Affiliations
University of Tübingen (DE)
ORCID
Venue
2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Publisher
IEEE
Published
2022-01
Pages
3686-3696
ISSN
2472-6737 (ISSN-L)
DOI
10.1109/wacv51458.2022.00374
OpenAlex
W3159166099
Cited by
253
References
5
Citations by year
2026: 67 · 2025: 82 · 2024: 52 · 2023: 39 · 2022: 10 · 2021: 3
FWCI
16.59
Type
conference paper
Language
en
Open access
Licence
Locations
  • IEEE Winter Conference on Applications of Computer Vision (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
Keywords
maritime search and rescue, unmanned aerial vehicles, object detection, object tracking, benchmark dataset

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