Model Parking Lot RGB DoFP Dataset

<magic ignore>Intro text here</magic ignore> The goal of this dataset is to generate visible spectro-polarimetric image data for complex scenes that is parameterized by solar, sensor, and scene geometry to support vehicle detection tasks. <ul> <li>A visible RGB division-of-focal-plane (DoFP) polarimeter is used to collect all data to allow for estimation of the spectro-polarimetric Stokes vector image data.</li> <li>A detailed terrain model of a parking lot setting was accurately designed to 1:64 scale that was 3D printed, painted, and based with modeling materials.</li> <li>Model vehicles were used to create 28 unique scenarios in dense (4), moderate (8), and sparse (16) configurations.</li> <li>All experiments are designed and autonomously conducted in the ASL's Automated Remote Sensing Solar Simulation (ARSSS) laboratory at the University of Dayton.</li> <li>The dataset contains 14,112 RGB Stokes vector images (provided in raw RGB DoFP modulated intensity format) across 28 different scenarios, resulting in 4,608 unique scene views, each under 14 different illumination geometries.</li> <li>Annotation data is provided for each of the 4,608 different scene views for each model vehicle class.</li> </ul>

Publication Details

Published
2025-07-14
DOI
https://doi.org/10.26890/tji3354
Primary Topic
Vehicle License Plate Recognition
Type
article
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Model Parking Lot RGB DoFP Dataset

Vehicle License Plate Recognition
article

Model Parking Lot RGB DoFP Dataset

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Abstract

<magic ignore>Intro text here</magic ignore> The goal of this dataset is to generate visible spectro-polarimetric image data for complex scenes that is parameterized by solar, sensor, and scene geometry to support vehicle detection tasks. <ul> <li>A visible RGB division-of-focal-plane (DoFP) polarimeter is used to collect all data to allow for estimation of the spectro-polarimetric Stokes vector image data.</li> <li>A detailed terrain model of a parking lot setting was accurately designed to 1:64 scale that was 3D printed, painted, and based with modeling materials.</li> <li>Model vehicles were used to create 28 unique scenarios in dense (4), moderate (8), and sparse (16) configurations.</li> <li>All experiments are designed and autonomously conducted in the ASL's Automated Remote Sensing Solar Simulation (ARSSS) laboratory at the University of Dayton.</li> <li>The dataset contains 14,112 RGB Stokes vector images (provided in raw RGB DoFP modulated intensity format) across 28 different scenarios, resulting in 4,608 unique scene views, each under 14 different illumination geometries.</li> <li>Annotation data is provided for each of the 4,608 different scene views for each model vehicle class.</li> </ul>

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