Toward Map Updates with Crosswalk Change Detection Using a Monocular Bus Camera

Detecting when road maps change is useful for autonomous vehicles to drive safely and legally, for city planners to make more educated decisions, and web maps to better serve consumers. Many public vehicles drive around the city on a regular basis and collect road data for security and safety purposes through dash cams, yet few cities and companies have considered this as a data source for city monitoring. We present an automatic method and system for crosswalk change detection at city intersections using a monocular camera on a city bus and analyze longitudinal results over the course of a year. Using images recorded by a bus two years ago as reference, multiple city intersections are reconstructed, fitted for ground planes, and labelled for crosswalks. Subsequent images from the bus are imported and processed to detect if changes have occurred since intersections were first seen by first localizing current images with respect to the reference images, detecting for crosswalks, and computing detection overlaps in the bird’s-eye-view. Our method makes improvements upon baseline methods by checking for crosswalk visibility and localization errors, is able to generate results typically seen by using more expensive LiDAR sensors, and has been successfully deployed live for one month.