Abstract
Given the advance of portable cameras, many vehicles are equipped with always-on cameras on their dashboards (referred to as dashcam). We aim to utilize these dashcam videos harvested in the wild to extract the driving behavior – global metric localization of 3D vehicle trajectory. We propose a robust approach to (1) extract a relative vehicle 3D trajectory from a dashcam video, (2) create a global metric 3D map using geo-localized Google StreetView RGBD panorama images, and (3) align the relative vehicle 3D trajectory to the 3D map to achieve global metric localization. We conduct an experiment on 50 dashcam videos captured in 11 cities in various time and under various road conditions. For each video, we uniformly sample at least 15 control point per road segment to manually annotate the ground truth 3D coordinate of the vehicle. Each extracted 3D trajectory is compared with these manually labeled ground truth 3D control points to calculate the error in meters. Our proposed method achieves a median error of $2.05$ meters and $85.5\%$ of them has error smaller than 5 meters. Our method significantly outperforms other vision-based baseline methods and is a more accurate alternative method than the most widely used consumer-level Global Positioning System (GPS).