Asynchronous Trajectory Matching-Based Multimodal Maritime Data Fusion for Vessel Traffic Surveillance in Inland Waterways

Abstract

The automatic identification system (AIS) and video cameras have been widely exploited for vessel traffic surveillance in inland waterways. The AIS data could provide vessel identity and dynamic information on vessel position and movements. In contrast, the video data could describe the visual appearances of moving vessels without knowing the information on identity, position, movements, etc. To further improve vessel traffic surveillance, it becomes necessary to fuse the AIS and video data to simultaneously capture the visual features, identity, and dynamic information for the vessels of interest. However, the performance of AIS and video data fusion is susceptible to issues such as data spatial difference, message asynchronous transmission, visual object occlusion, etc. In this work, we propose a deep learning-based simple online and realtime vessel data fusion method (termed DeepSORVF). We first extract the AIS-and video-based vessel trajectories, and then propose an asynchronous trajectory matching method to fuse the AIS-based vessel information with the corresponding visual targets. In addition, by combining the AIS-and video-based movement features, we also present a prior knowledge-driven anti-occlusion method to yield accurate and robust vessel tracking results under occlusion conditions. To validate the efficacy of our DeepSORVF, we have also constructed a new benchmark dataset (termed FVessel) for vessel detection, tracking, and data fusion. It consists of many videos and the corresponding AIS data collected in various weather conditions and locations. The experimental results have demonstrated that our method is capable of guaranteeing high-reliable data fusion and anti-occlusion vessel tracking. The DeepSORVF code and FVessel dataset are publicly available at https://github.com/gy65896/DeepSORVF and https://github.com/gy65896/FVessel, respectively.

Ship Detection under Low-Visibility Weather Interference via an Ensemble Generative Adversarial Network

“…Similar studies can be found in (Guo et al, Bai et al. [20,21]). The acquisition of multi-source data usually depends on special physical sensors, which are highly susceptible to the water environment and have high maintenance costs (Shang et al, Lin et al [22,23]).…”

Association of AIS and Radar Data in Intelligent Navigation in Inland Waterways Based on Trajectory Characteristics

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A Rust Extraction and Evaluation Method for Navigation Buoys Based on Improved U-Net and Hue, Saturation, and Value

“…With the development of computer vision technology, object detection has become one of the research hotspots in the field of computer vision [3,4] and has received widespread attention in fields such as power inspection [5] and road traffic [6]. In the field of navigation, the recognition of targets such as ships and sea skylines is the focus of research [7,8], but there is relatively little research on the detection of navigation aids. Unmanned equipment, such as drones and unmanned ships equipped with visual sensors, is used to obtain image data during the inspection.…”