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 the vessel identity and dynamic information on vessel position and movements. In contrast, the video data could describe the visual appearances of moving vessels, but without knowing the information on identity, position and 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, traditional data fusion methods easily suffer from several potential limitations, e.g., asynchronous messages, missing data, random outliers, etc. In this work, we first extract the AIS- and video-based vessel trajectories, and then propose a deep learning-enabled asynchronous trajectory matching method (named DeepSORVF) 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.
Abstract (translated)
自动识别系统(AIS)和摄像头已经被广泛应用于内陆水路中的船只流量监控。AIS数据可以提供船只身份和关于船只位置和移动的动态信息。相比之下,视频数据可以描述移动船只的视觉外观,但不知道身份、位置和移动等信息。为了进一步提高船只流量监控,必须将AIS和视频数据合并,同时捕捉感兴趣的船只的视觉特征、身份和动态信息。然而,传统的数据融合方法很容易受到多种潜在限制,例如异步消息、缺失数据、随机异常值等。在本文中,我们首先提取了基于AIS和视频的船只轨迹,然后提出了一种具有深度学习功能的异步轨迹匹配方法(名为DeepSORVF),以将基于AIS的船只信息与相应的视觉目标进行合并。此外,通过结合AIS和视频的运动特征,我们还提出了一种基于先验知识的动力排斥方法,以在遮挡条件下产生准确的、可靠的船只跟踪结果。为了验证我们的DeepSORVF的有效性,我们还建立了一个新的基准数据集(名为FVessel),用于船只检测、跟踪和数据融合。它包括在各种天气条件和位置收集的许多视频和相应的AIS数据。实验结果显示,我们的方法能够保证高可靠性的数据融合和排斥船只跟踪。
URL
https://arxiv.org/abs/2302.11283
