Traffic Sign Localization Using SfM and Deep Learning

This study addresses the challenge of traffic sign inventory management faced by the U.S. Department of Transportation in complying with Manual on Uniform Traffic Control Devices (MUTCD) standards. The study proposes a cost-effective methodology for geo-localizing traffic signs using smartphone-recorded video and GPS data. The approach employs various techniques, including depth estimation deep learning models, and Structure-from-Motion (SfM), to accurately determine the geographic coordinates of roadside traffic signs. The methodology was tested in diverse environments, including challenging mountain roads with curves and urban settings. Structure-from-Motion (SfM) is shown as the most effective approach, demonstrating high accuracy with 90.91% of tested signs (40 out of 44) in Pima County, Arizona, and 88.24% of tested signs (90 out of 102) in Peyton Road, Atlanta, Georgia, achieving a distance error below 4.9 meters. The remaining discrepancies were mainly caused by GPS inaccuracies rather than the limitations. These results show SfM as a promising solution for efficient and accurate traffic sign geo-localization.

Date

2024-12-08

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Text

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