Paper Info
Title
Extracting Driving Behavior: Global Metric Localization from Dashcam Videos in the Wild.
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 trajectories (Fig. 1). 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 panoramic 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 under various traffic conditions. For each video, we uniformly sample at least 15 control frames per road segment to manually annotate the ground truth 3D locations of the vehicle. On control frames, the extracted 3D locations are compared with these manually labeled ground truths to calculate the distance in meters. Our proposed method achieves an average error of 2.05 m and (85.5,%) of them have error no more than 5 m. 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).
Year
DOI
Venue
2016
10.1007/978-3-319-46604-0_10
ECCV Workshops
Keywords
Field
DocType
Camera localization,Structure from motion
Structure from motion,Computer vision,Computer science,Ground truth,Artificial intelligence,Global Positioning System,Dashboard (business),Traffic conditions,Trajectory
Conference
Citations 
PageRank 
References 
0
0.34
19
Authors
6
Name
Order
Citations
PageRank
Shao-Pin Chang100.34
Jui-Ting Chien2343.09
Fu-En Wang371.77
Shang-Da Yang400.34
Hwann-Tzong Chen582652.13
Min Sun6108359.15