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.

中文翻译：

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航道身份识别：一种基于多模态数据的船舶信息跟踪新方法

视频监控是船舶交通监管的重要手段。在实践中，监管机构通常需要使用电子海图平台来确定有关通过视频馈送的船舶的基本信息。为了丰富监控视频中的信息并有效利用多模态海事数据，本文提出了一种基于改进的单发多盒检测器（SSD）和DeepSORT算法的新型船舶多目标跟踪技术。此外，利用夜间对比度增强算法增强夜间场景中的船舶识别性能，并采用多模态数据融合算法将船舶自动识别系统（AIS）信息融入视频显示。实验结果表明，船舶信息在白天和夜间场景的跟踪精度分别为78⋅2%和70⋅4%，分别。我们的方法可以有效地帮助监管机构从视频中快速获取船舶信息，提高对航道的监管。