Mathematical models for metric features extraction from RGB-D sensor

Elton Fernandes Dos Santos,Scheila Geiele Kamchen,Isabella C F S Condotta,Luciano Bastos Lopes,Laurimar Gonçalves Vendrusculo

doi:10.36560/141120211467

Abstract

The use of the RGB-D camera has been applied in several fields of science. That popularization is due to the emergence of technologies such as the Intel® RealSense™ D400 series. However, despite the actual demand from some potential users, few studies concern the characterization of these sensors for object measurements. Our study sought to estimate models dealing with calculating the area and length between targets or points within RGB and depth images. An experiment was set up with white cardboard fixed on a flat surface with colored pins. We measured the distance between the camera and cardboard by calculating the average distance from the pixels belonging to the target area. The Information Criterion AIC and BIC associated with R2 were performed to select the best models. Polynomial and power regression models reached the highest coefficient of determination and smallest values of AIC and BIC.

Highlights

Even though RGB-D sensors were extensively employed in the past decade, mainly to promote human interaction through video game consoles, their use for object detection and metric measurements in productive activities has been in the early stages
In order to obtain the relation between the target pixel area and depth images at the RGB-D camera, an experiment was set up: a white cardboard was fixed to a flat surface with colored pins (Fig. 1), perpendicular to the camera
RGB-D sensor is becoming essential to diverse industrial and agricultural applications that need to measure the within area and distance to specific targets

Summary

Introduction

Even though RGB-D sensors were extensively employed in the past decade, mainly to promote human interaction through video game consoles, their use for object detection and metric measurements in productive activities has been in the early stages. Most RGB-D information is analyzed through regression or machine learning models to estimate metric measurements, surface area, and volume Focusing on this prominent market, Intel® launched the RealSenseTM D400 series of RGB-D cameras, providing colored image flow and depth maps. These cameras rely on an infrared light source, which brings improvements in data quality, allowing the most reliable outdoor applications, such as a tropical environment with a high luminous variation (Condotta et al, 2020). The approach implemented by Vit and Shani et al.,2018 used Euclidean distance in the 3D plane to measure artificial objects' size while Hu et al, 2021 extracted length and width from RGB-D images of non-restrained pigs using primary pixel count

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