An approach for accurately extracting vehicle trajectory from aerial videos based on computer vision

Vehicle trajectory data holds valuable information for advanced driving development and traffic analysis. While unmanned aerial vehicle (UAV) offer a broader perspective, the detection of small-scale vehicles in video frames still suffers from low accuracy or is even missed. This study proposes a comprehensive technical framework for accurate vehicle trajectory extraction, encompassing six main components: video stabilization, vehicle detection, vehicle tracking, lane marking detection, coordinate transformation, and data denoising. To mitigate video jitter, the SURF and FLANN stabilization algorithms are utilized. An enhanced detector based on You Only Look Once X (YOLOX) is employed for multi-target vehicle detection, incorporating a shallow feature extraction module within the detection head to improve the performance for low-level and small-scale features. Efficient Channel Attention (ECA) modules are integrated before the neck to further boost the expressiveness. Additionally, a sliding window inference method is applied at the input stage to prevent compression of high-resolution video frames. The Savitzky-Golay filter is used for trajectory noise reduction. Verification results demonstrate that the improved YOLOX achieves a mean average precision (mAP) of 88.7 %, an enhancement of 5.6 % over the original model. When compared to advanced YOLOv7 and YOLOv8 models, the proposed method increases mAP@50 by 7.63 % and 1.07 %, respectively. The Mostly Tracked (MT) trajectories metric reaches 98.9 %, and the root-mean-square error of one-sided localization is approximately 0.05 m. These results confirm that the proposed framework is an effective tool for high-accuracy vehicle trajectory data collection in traffic studies. Additionally, a vehicle trajectory dataset has been developed and is publicly accessible at www.cqskyeyex.com.