The Swarm Intelligence Freeway-Urban Trajectories (SWIFTraj) Dataset - Part II: A Graph-Based Approach for Trajectory Connection

2026-02-25

AI Summary

This work addresses the challenge of connecting vehicle trajectories across videos captured by a drone swarm, where temporal misalignment and irregular spatial configurations hinder consistent tracking. To overcome this, the authors propose a graph-based trajectory association method that models the drone formation as an undirected graph to flexibly capture its spatial layout. They introduce an automatic time alignment strategy that minimizes trajectory matching costs and employ the Hungarian algorithm to achieve cross-video vehicle correspondence. This study is the first to integrate graph structures into collaborative perception with drone swarms, achieving high-precision temporal alignment (error < 3 frames) and vehicle matching (F1-score = 0.99), thereby successfully reconstructing continuous vehicle trajectories exceeding 4.5 kilometers in length.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

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.

Problem

• trajectory connection
• UAV swarm
• time alignment
• vehicle trajectory
• graph-based approach

Innovation

• graph-based trajectory connection
• UAV swarm
• time alignment
• vehicle matching
• trajectory dataset