A framework uses standardized US license plate typography and geometry as passive fiducials for metric monocular distance, velocity, and time-to-collision estimation without machine learning training.
Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-Shot Cross- Dataset Transfer
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Physics-Grounded Monocular Vehicle Distance Estimation Using Standardized License Plate Typography
A framework uses standardized US license plate typography and geometry as passive fiducials for metric monocular distance, velocity, and time-to-collision estimation without machine learning training.