High-Precision Trajectory Extraction Using Advanced Computer Vision From a Multi-Drone Experiment in South Korea

In this study, we develop a complete framework for accurate trajectory extraction for multi-drone experiments with an application in the Songdo International Business District, South Korea. The raw bird’s eye view drone footage was processed and segmented, resulting in a substantial database of 4K videos. We annotate a total of 5,419 video frames to create the exhaustive Songdo visual dataset, a valuable resource for object detection validation. Our methodology involves a transformation of raw drone footage into comprehensive traffic data via a robust vehicle trajectory extraction pipeline. Throughout the experiment, meticulous procedures were employed to ensure data integrity and precision. Key findings from the study underline the potential of drone technology combined with computer vision tools for reliable and detailed traffic monitoring in smart cities. Notably, we leveraged the high-precision autonomous vehicle trajectories from Stanford to quantitatively validate our pipeline, further reinforcing the robustness of our results. As part of our commitment to open science, we plan to release both the Songdo visual and trajectory datasets to the public.