蜂群智能高速公路-城市轨迹（SWIFTraj）数据集——第二部分：基于图的方法用于轨迹连接
The Swarm Intelligence Freeway-Urban Trajectories (SWIFTraj) Dataset -- Part II: A Graph-Based Approach for Trajectory Connection
AI总结：
本文提出基于图的方法，解决无人机群轨迹连接中的时间对齐和车辆匹配问题，通过模拟和真实数据验证，实现高精度轨迹连接。
摘要：
在本系列论文第一部分中，我们介绍了SWIFTraj，一个通过无人机群收集的新开源车辆轨迹数据集。该数据集有两个显著特点：首先，通过连接连续无人机视频中的轨迹，提供长距离连续轨迹，最长超过4.5公里；其次，涵盖由高速公路及其连接的城市道路组成的综合交通网络。从无人机群获取如此长距离的连续轨迹具有挑战性，因为需要在多个视频之间进行准确的时间对齐，并且无人机的空间分布不规则。为解决这些挑战，本文提出了一种新颖的基于图的方法用于连接无人机群捕获的车辆轨迹。构建了一个无向图来表示灵活的无人机布局，并开发了一种基于轨迹匹配成本最小化的自动时间对齐方法，以估计视频之间的最佳时间偏移。为了关联不同视频中同一辆车的轨迹，使用匈牙利算法建立了车辆匹配表。所提出的方法在模拟和真实数据上进行了评估。实世界实验的结果显示，时间对齐误差在三个视频帧内，对应约0.1秒，且车辆匹配的F1分数约为0.99。这些结果证明了所提出方法在解决无人机轨迹连接中的关键挑战的有效性，并突显了其在大规模车辆轨迹收集中的潜力。
英文摘要：
In Part I of this companion paper series, we introduced SWIFTraj, a new open-source vehicle trajectory dataset collected using a unmanned aerial vehicle (UAV) swarm. The dataset has two distinctive features. First, by connecting trajectories across consecutive UAV videos, it provides long-distance continuous trajectories, with the longest exceeding 4.5 km. Second, it covers an integrated traffic network consisting of both freeways and their connected urban roads. Obtaining such long-distance continuous trajectories from a UAV swarm is challenging, due to the need for accurate time alignment across multiple videos and the irregular spatial distribution of UAVs. To address these challenges, this paper proposes a novel graph-based approach for connecting vehicle trajectories captured by a UAV swarm. An undirected graph is constructed to represent flexible UAV layouts, and an automatic time alignment method based on trajectory matching cost minimization is developed to estimate optimal time offsets across videos. To associate trajectories of the same vehicle observed in different videos, a vehicle matching table is established using the Hungarian algorithm. The proposed approach is evaluated using both simulated and real-world data. Results from real-world experiments show that the time alignment error is within three video frames, corresponding to approximately 0.1 s, and that the vehicle matching achieves an F1-score of about 0.99. These results demonstrate the effectiveness of the proposed method in addressing key challenges in UAV-based trajectory connection and highlight its potential for large-scale vehicle trajectory collection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
