Proceedings of the 2018 3rd International Conference on Modelling, Simulation and Applied Mathematics (MSAM 2018)

Moving Ship Detection Algorithm Based on Gaussian Mixture Model

Authors
Zuohuan Chen, Jiaxuan Yang, Zhen Kang
Corresponding Author
Zuohuan Chen
Available Online July 2018.
DOI
https://doi.org/10.2991/msam-18.2018.42How to use a DOI?
Keywords
traffic engineering; target detection; gaussian mixture model; moving ship; background subtraction; target segmentation
Abstract
In order to reduce the influence of moving objects clutter in the background on the ship objects detection from ship video surveillance and improve the reliability of ship targets detection, this paper presents a method of ship objects detection using Gaussian mixture model. A Gaussian mixture model is established to estimate the background. The new pixel, in the video, which does not match the Gaussian distributions is regarded as foreground, otherwise background. The moving ship targets are detected by the continuity of the current and former frames, in which the foreground is obtained by subtracting the background from ship video. The target precision rate of the algorithm is 100% and the false alarm probability is 3.02% in the simulation experiment. Comparing with other algorithms, the results show that this algorithm can not only improve target precision rate, but also reduce false alarm probability, and greatly overcome the influence of large amount of clutter on the detection of moving ship objects in video background, effectively restraining the influence of the noise from the dynamic scenario transformation.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Cite this article

TY  - CONF
AU  - Zuohuan Chen
AU  - Jiaxuan Yang
AU  - Zhen Kang
PY  - 2018/07
DA  - 2018/07
TI  - Moving Ship Detection Algorithm Based on Gaussian Mixture Model
BT  - Proceedings of the 2018 3rd International Conference on Modelling, Simulation and Applied Mathematics (MSAM 2018)
PB  - Atlantis Press
SP  - 197
EP  - 201
SN  - 1951-6851
UR  - https://doi.org/10.2991/msam-18.2018.42
DO  - https://doi.org/10.2991/msam-18.2018.42
ID  - Chen2018/07
ER  -