Marine Vessel Tracking using a Monocular Camera

Tobias Jacob, Raffaele Galliera, Muddasar Ali, Sikha Bagui

2021

Abstract

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 being also able to track multiple different vessels.

Paper Citation

in Harvard Style

Jacob T., Galliera R., Ali M. and Bagui S. (2021). Marine Vessel Tracking using a Monocular Camera. In Proceedings of the 2nd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA, ISBN 978-989-758-526-5, pages 17-28. DOI: 10.5220/0010516000170028

in Bibtex Style

@conference{delta21,
author={Tobias Jacob and Raffaele Galliera and Muddasar Ali and Sikha Bagui},
title={Marine Vessel Tracking using a Monocular Camera},
booktitle={Proceedings of the 2nd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,},
year={2021},
pages={17-28},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010516000170028},
isbn={978-989-758-526-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,
TI - Marine Vessel Tracking using a Monocular Camera
SN - 978-989-758-526-5
AU - Jacob T.
AU - Galliera R.
AU - Ali M.
AU - Bagui S.
PY - 2021
SP - 17
EP - 28
DO - 10.5220/0010516000170028