# Application of Augmented Reality in Waterway Traffic Management Using Sparse Spatiotemporal Data
## 2 Related Work
By integrating visual object tracking with segmentation and leveraging a refined feature pyramid network (FPN) and a multidimensional attention module (MDAM), the method effectively addressesissues such as scale variation and edge information loss. This approach significantly improves tracking accuracy and robustness, achieving state-of-the-art results on the LMD-TShips dataset [27]. [PERSON] et al. proposed the PF-TBD method based on spectral residual bivariate entropy, enabling target detection and tracking without prior knowledge of sea clutter by extracting salient maps and calculating information entropy [28]. [PERSON] et al. developed the SiamFPN deep learning tracking method, modifying the Siamese network, combining candidate region selection with historical influences, and facilitating real-time maritime vessel tracking [29]. [PERSON] introduces LMD-TShip, a large-scale, high-resolution benchmark dataset for maritime ship tracking, designed to provide effective means for training and testing ship-tracking models in autonomous navigation applications. Through a detailed analysis of the dataset's characteristics and challenging attributes, it lays the foundation for developing advanced maritime ship-tracking methods [30]. [PERSON] reviews real-time multi-object detection and tracking technology based on YOLO and DeepSORT, analyzing the application, advantages, and disadvantages of different algorithms in object detection, classification, and tracking and exploring the practical application potential of these technologies in various fields [31]. The complexity and high traffic density of inland waterways demand multi-source data fusion technologies to enhance overall perception capabilities and decision support. By integrating data from different sensors, the comprehensive monitoring and precise localization of vessels can be achieved, thereby improving waterway efficiency and safety. In recent years, researchers have proposed various methods to fuse AIS data and visual data in order to enhance perception capabilities in inland waterways. For example, [PERSON] presents a method for tracking a vessel by combining video surveillance and shipborne AIS (automatic identification system) reports. It dynamically adjusts the camera's focus and orientation and applies a Kalman filter to smooth video images, achieving the automatic positioning and tracking of specific vessels while enhancing the tracking accuracy and user-friendliness of video surveillance systems [32]. [PERSON], based on AIS and video data, employs the Kalman filter algorithm to fuse the two data sources, optimizing vessel trajectory estimation [33]. [PERSON] presents a maritime surveillance framework that fuses camera-based vessel detection with the automatic identification system (AIS) to achieve accurate and reliable vessel identification, providing effective technical support for maritime safety monitoring [34]. [PERSON] presents a multimodal maritime data fusion method based on asynchronous trajectory matching for vessel traffic surveillance in inland waterways, utilizing deep learning techniques to integrate automatic identification system (AIS) and video data in order to enhance the accuracy and robustness of vessel detection, tracking, and data fusion [35]. [PERSON] presents a method for assessing the risk of vessel collision in inland waterways based on the fusion of AIS and visual data, effectively improving the accuracy and reliability of collision risk assessment by integrating the strengths of both data sources and providing more reliable decision support for navigation safety in inland waterways [36]. [PERSON] proposes an anti-occlusion tracking algorithm based on visual trajectory analysis, which uses multi-dimensional feature analysis and the Hungarian algorithm to match data from different sensors, effectively reducing detection omissions caused by vessel overlap [37]. [PERSON] et al.