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.

翻译：在本系列论文的第一部分中，我们介绍了SWIFTraj——一种利用无人机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：首先，通过连接连续无人机视频中的轨迹，提供超过4.5公里的长距离连续轨迹；其次，它覆盖了由高速公路及其相连城市道路组成的综合交通网络。由于需要跨多段视频进行精确时间对齐，且无人机空间分布不规则，从无人机集群中获取此类长距离连续轨迹面临挑战。为解决这些问题，本文提出了一种新颖的基于图的方法来连接无人机集群捕获的车辆轨迹。通过构建无向图以灵活表示无人机布局，并开发基于轨迹匹配代价最小化的自动时间对齐方法，可估算视频间的最优时间偏移量。为关联不同视频中同一车辆的轨迹，采用匈牙利算法建立车辆匹配表。本文使用仿真数据和真实数据对所提方法进行评估。真实世界实验结果表明，时间对齐误差在三帧视频以内（约0.1秒），车辆匹配的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轨迹连接关键挑战中的有效性，并凸显了其在大规模车辆轨迹采集领域的潜力。