# Application of Augmented Reality in Waterway Traffic Management Using Sparse Spatiotemporal Data
## 5 Experiments
### Multi-Source Data Fusion Experiment
Based on this division, we also partition the video pixel matrix into six corresponding regions. The specific region division results are presented in Figure 12.  
After the region division, we determine the region set where the vessel is located based on its polar coordinates within the camera's view and perform matching in the pixel coordinate system. When multiple vessels are present in a region set, as shown in Figure 13, and three vessels are located within one region, we rank their features in ascending order of angular differences from the camera's baseline angle, \(\theta_{0}+\Delta\theta\). Specifically, the vessels are ranked as \(\mathrm{v}_{1}^{(\mathrm{P}_{2})},\mathrm{v}_{2}^{(\mathrm{P}_{2})},\mathrm{v }_{3}^{(\mathrm{P}_{2})}\), and matching is performed in the pixel coordinate system. The matching results are further verified by the distance to the camera. Figure 9: Comparison of ship detection in foggy channel environments: (a) ship detection effect on original foggy image; (b) ship detection effect after image dehazing. Figure 11: Comparison of real trajectory and predicted trajectory. Figure 10: Comparison of ship detection in nighttime channel environments: (**a**) ship detection effect on original nighttime image; (**b**) ship detection effect after image illumination enhancement. The selected video data include multiple detected vessels and ships detected at long distances, representing a complex inland waterway environment. The fusion results are shown in Figure 14 below:  
Figure 14: Fusion effect display. Figure 6:0 Perspective area division: (**a**) the camera’s field of view is divided into six smaller sub-regions; (**b**) the video matrix is also divided into six sub-regions according to the number of divisions made via the camera. Figure 6:1 Pixel coordinate ship matching process: within the region set \(P_{2}\), there are a total of three ships. They are matched sequentially based on their distance from the set baseline. The Table 6 below presents various fusion evaluation metrics. The results demonstrate that the proposed method achieves stable fusion performance, with fusion accuracy ranging from 84.29% to 94.32%. It also performs well in the IDP, IDR, and IDF\({}_{1}\) metrics. These findings highlight that considering the trajectory displacement direction of both AIS and video-detected vessels significantly improves fusion performance. Additionally, our target matching method further enhances fusion effectiveness. Waterway Traffic Situation Awareness AR Information Display Framework  
This section aims to enhance waterway traffic awareness for vessels through the design of an augmented reality (AR) framework for waterway traffic situation awareness. Figure 15 presents a subset of results over three consecutive days. The framework enables the intuitive retrieval of AIS information for vessels detected in the video, including the vessel type, name, speed, and heading, allowing for a direct comparison between recognized vessel types and AIS data to verify the effectiveness of the augmented reality framework. Additionally, to offer a more intuitive understanding of waterway perception information, we integrated a dynamic navigation information display module. This module provides the number of vessels tracked in the current frame, their average speed, and facilitates better assessment of the current traffic conditions. It also includes a vessel flow counting function, calculating the number of vessels passing through the waterway in the past hour as part of a vessel density statistics module to capture traffic flow information. Furthermore, we predict the next period of navigation pressure based on historical periodic variations in waterway vessel traffic, helping relevant authorities prepare in advance and provide navigation services to vessels.