# Marine vessel tracking using a monocular camera

arXiv: https://arxiv.org/abs/2108.10367 · HTML: https://mlsanity.com/paper/2108.10367/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 23 Aug 2021.

## Builds on (most cited first, 7 of 7)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [A Survey of Deep Learning-based Object Detection](https://mlsanity.com/paper/1907.09408/) (2019)
- [Joint Monocular 3D Vehicle Detection and Tracking](https://mlsanity.com/paper/1811.10742/) (2018)
- [Monocular Depth Estimation: A Survey](https://mlsanity.com/paper/1901.09402/) (2019)
- [Real-time 3D Pose Estimation with a Monocular Camera Using Deep Learning and Object Priors On an Autonomous Racecar](https://mlsanity.com/paper/1809.10548/) (2018)

## Built on by (most cited first, 2 of 2)

- [MARLIN: Soft Actor-Critic based Reinforcement Learning for Congestion Control in Real Networks](https://mlsanity.com/paper/2302.01301/) (2023)
- [Adaptive Entropy-Driven Sensor Selection in a Camera-LiDAR Particle Filter for Single-Vessel Tracking](https://mlsanity.com/paper/2603.08457/) (2026)

## 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.