  Vehicle trajectory data provides critical information for traffic flow
modeling and analysis. Unmanned aerial vehicles (UAV) is an emerging technology
for traffic data collection because of its flexibility and diversity on spatial
and temporal coverage. Vehicle trajectories are constructed from frame-by-frame
detections. The increase of vehicle counts makes multiple-target matching more
challenging. Errors are caused by pixel jitter, vehicle shadows, road marks as
well as some missing detections. This research proposes a novel framework for
construction of massive vehicle trajectories from aerial videos by matching
vehicle detections based on traffic flow dynamic features. The You Look Only
Once (YOLO) v4 is used for vehicle detection in UAV videos based on Convolution
Neural Network (CNN). Trajectory construction is proposed in detected bounding
boxes with trajectory identification, integrity enhancement, and coordinate
transformation from image coordinates to the Frenet coordinates. The raw
trajectory obtained is then denoised by the ensemble empirical mode
decomposition (EEMD). Our framework is tested on two aerial videos taken by a
UAV on city expressway covering congested and free-flow traffic conditions. The
results show that the proposed framework achieves a Recall of 93.00% and
86.69%, and a Precision of 98.86% and 98.83% for vehicle trajectories in the
free-flow and congested traffic conditions.The trajectory processing speed is
about 30s per track.
