Mapping License Plate Recoverability Under Extreme Viewing Angles for Opportunistic Urban Sensing

Urban environments contain many imaging sensors built for specific purposes, including ATM, body-worn, CCTV, and dashboard cameras.

Under the opportunistic sensing paradigm, these sensors can be repurposed for secondary inference tasks such as license plate recognition.

Yet objects of interest in such im-agery are often noisy, low-resolution, and captured from extreme viewpoints.

Recent advances in AI-based restoration can recover useful information even from severely degraded images.

A central challenge is de-termining which distortion parameters allow reliable recovery and which lead to inference failure.

This paper introduces recoverability maps, a task-agnostic method for quantifying this boundary.

The method combines a dense synthetic sweep of degradation parameters with two summary measures: boundary ar-ea-under-curve, which estimates the recoverable fraction of the parameter space, and a reliability score, which captures the frequency and severity of failures within that region.

We demonstrate the method on li-cense plate recognition from highly angled views under realistic camera artifacts.

Several restoration archi-tectures are trained and evaluated, including U-Net, Restormer, Pix2Pix, and SR3 diffusion.

The best model recovers about 93% of the parameter space.

Similar results across models suggest that sensing geometry, ra-ther than architecture, sets the limit of recovery.