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Maritime traffic situation awareness analysis via high-fidelity ship imaging trajectory

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Abstract

Situation awareness provides crucial yet instant information to maritime traffic participants, and significant attentions are paid to implement traffic situation awareness task via various maritime data source (e.g., automatic identification system, maritime surveillance video, radar, etc.). The study aims to analyze traffic situation with the support of ship imaging trajectory. First, we employ the dark channel prior model to remove fog in maritime videos to obtain high-resolution ship images (i.e., fog-free maritime images). Second, we track ships in each maritime image with the scale adaptive kernel correlation filter (SAMF), and thus obtain raw ship imaging trajectories. Third, we cleanse abnormal ship trajectory samples via curve-fitting and down sampling method, and thus further maritime traffic situation analysis is implemented. We analyze maritime traffic situation in three typical videos (i.e., three typical maritime traffic scenarios), and experimental results suggested that the proposed framework can extract high-resolution ship imaging trajectory for fulfilling the task of accurate maritime traffic situation awareness.

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Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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Acknowledgements

This work was jointly supported by National Key R&D Program of China (2021YFC2801002), National Natural Science Foundation of China (52102397, 52071200, 62176150), China Postdoctoral Science Foundation (2022M712027), Fund of National Engineering Research Center for Water Transport Safety (A2022003).

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Authors and Affiliations

  1. Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai, 201306, People’s Republic of China

    Xinqiang Chen, Jinbiao Zheng & Chaofeng Li

  2. Intelligent Transportation System Research Center (ITSC), Wuhan University of Technogloy, Wuhan, 430063, China

    Bing Wu

  3. State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan, 430063, China

    Bing Wu

  4. Merchant Marine College, Shanghai Maritime University, Shanghai, 201306, People’s Republic of China

    Huafeng Wu

  5. Research Group on Maritime Transportation Risk and Safety, Gdynia Maritime University, Morska 81-87, 81-225, Gdynia, Poland

    Jakub Montewka

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  1. Xinqiang Chen
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Correspondence to Bing Wu.

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Chen, X., Zheng, J., Li, C. et al. Maritime traffic situation awareness analysis via high-fidelity ship imaging trajectory. Multimed Tools Appl 83, 48907–48923 (2024). https://doi.org/10.1007/s11042-023-17456-6

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  • DOI: https://doi.org/10.1007/s11042-023-17456-6

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