Marine vessel tracking using a monocular camera
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Abstract (arXiv)
In this paper, a new technique for camera calibration using only GPS data is presented. A new way of tracking objects that move on a plane in a video is achieved by using the location and size of the bounding box to estimate the distance, achieving an average prediction error of 5.55m per 100m distance from the camera. This solution can be run in real-time at the edge, achieving efficient inference in a low-powered IoT environment while also being able to track multiple different vessels.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 7 of its 22 references that are in the map. First posted on arXiv 23 Aug 2021.
Builds on
7 of 22 references.
References · 7
- 2014Microsoft COCO: Common Objects in Contextcited by 17,498
- 2019EfficientDet: Scalable and Efficient Object Detectioncited by 1,018
- 2017Simple Online and Realtime Tracking with a Deep Association Metriccited by 908
- 2019A Survey of Deep Learning-based Object Detectioncited by 106
- 2018Joint Monocular 3D Vehicle Detection and Trackingcited by 77
- 2019Monocular Depth Estimation: A Surveycited by 37
- 2018Real-time 3D Pose Estimation with a Monocular Camera Using Deep Learning and Object Priors On an Autonomous Racecarcited by 1
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Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 1.
2 papers.