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Identity recognition on waterways: a novel ship information tracking method based on multimodal data

Published online by Cambridge University Press:  25 June 2021

Zishuo Huang ,
Qinyou Hu ,
Qiang Mei ,
Chun Yang  and
Zheng Wu
Zishuo Huang
Affiliation:
Merchant Marine College, Shanghai Maritime University, Shanghai, China.
Qinyou Hu*
Affiliation:
Merchant Marine College, Shanghai Maritime University, Shanghai, China.
Qiang Mei
Affiliation:
Merchant Marine College, Shanghai Maritime University, Shanghai, China. Navigation College, Jimei University, Xiamen, China.
Chun Yang
Affiliation:
Merchant Marine College, Shanghai Maritime University, Shanghai, China.
Zheng Wu
Affiliation:
Department of Mathematics and Computer Science, Information Engineering University, Zhengzhou, China
*
*Corresponding author. E-mail: qyhu@shmtu.edu.cn
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Abstract

Video monitoring is an important means of ship traffic supervision. In practice, regulators often need to use an electronic chart platform to determine basic information concerning ships passing through a video feed. To enrich the information in the surveillance video and to effectively use multimodal maritime data, this paper proposes a novel ship multi-object tracking technology based on improved single shot multibox detector (SSD) and DeepSORT algorithms. In addition, a night contrast enhancement algorithm is used to enhance the ship identification performance in night scenes and a multimodal data fusion algorithm is used to incorporate the ship automatic identification system (AIS) information into the video display. The experimental results indicate that the ship information tracking accuracies in the day and night scenes are 78⋅2% and 70⋅4%, respectively. Our method can effectively help regulators to quickly obtain ship information from a video feed and improve the supervision of a waterway.

Keywords

ship information trackingmultimodal dataobject detectionobject tracking

Information

Type
Research Article
Information
The Journal of Navigation , Volume 74 , Issue 6 , November 2021 , pp. 1336 - 1352
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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