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Authors: Tobias Jacob ; Raffaele Galliera ; Muddasar Ali and Sikha Bagui

Affiliation: Department of Computer Science, University of West Florida, Pensacola, Florida, U.S.A.

Keyword(s): Camera Calibration, Monocular Object Tracking, Object Detection, Error Prediction, Edge-ml, Distance Estimation, YoloV5, Kalman Filters.

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.

CC BY-NC-ND 4.0

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Paper citation in several formats:
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 - DeLTA; ISBN 978-989-758-526-5; ISSN 2184-9277, SciTePress, pages 17-28. DOI: 10.5220/0010516000170028

@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 - DeLTA},
year={2021},
pages={17-28},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010516000170028},
isbn={978-989-758-526-5},
issn={2184-9277},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Deep Learning Theory and Applications - DeLTA
TI - Marine Vessel Tracking using a Monocular Camera
SN - 978-989-758-526-5
IS - 2184-9277
AU - Jacob, T.
AU - Galliera, R.
AU - Ali, M.
AU - Bagui, S.
PY - 2021
SP - 17
EP - 28
DO - 10.5220/0010516000170028
PB - SciTePress