在本系列配套论文的第一部分中，我们介绍了SWIFTraj——一种利用无人机（UAV）集群采集的新型开源车辆轨迹数据集。该数据集具有两个显著特征：其一，通过连接连续无人机视频中的轨迹，提供长距离连续轨迹，其中最长轨迹超过4.5 km；其二，覆盖包含高速公路及其相连城市道路的综合交通网络。由于需实现多段视频间的精确时间对齐，且无人机空间分布不规则，从无人机集群获取此类长距离连续轨迹面临挑战。为此，本文提出一种新颖的基于图的方法，用于连接无人机集群所捕获的车辆轨迹。该方法构建无向图以表征灵活的无人机布设布局，并开发了一种基于轨迹匹配代价最小化的自动时间对齐方法，以估计各视频间最优时间偏移量。为关联不同视频中同一车辆的轨迹，采用匈牙利算法构建车辆匹配表。所提方法在仿真数据与真实数据上均进行了评估。真实实验结果表明，时间对齐误差控制在三帧视频内（约0.1 s），车辆匹配F1分数约为0.99。这些结果验证了所提方法在解决基于无人机的轨迹连接关键挑战方面的有效性，并凸显其在大规模车辆轨迹采集中的应用潜力。

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.