VDIS: A System for Morphological Detection & Identification of Vehicles in RGB images

Authors

  • Ron Mahabir George Mason University
  • Kevin Gonzales George Mason University
  • Jared Silk George Mason University

DOI:

https://doi.org/10.13021/G8jmgr.v2i2.549

Keywords:

Vehicle detection, vehicle identification, traffic, image spectra

Abstract

With the growth of urban centers worldwide, the number of vehicles in and around these areas has also increased. Traffic-related data plays an important role in spatial planning, for example, optimizing road networks and in the estimation or simulation of air and noise pollution. This information is important as it reflects the changes taking place around us. Additionally, data collected can be used for a wide array of applications including law enforcement, fleet management, and supporting other analyses at varying scales. In this paper, we present a method for the detection and identification of vehicles from low altitude, high spatial resolution Red Blue Green (RGB) images, utilizing both object spectra and image morphology. Results show an identification performance upwards of 62% with false positives occurring from the use of images with sun glare and vehicles with similar spectra values.

Author Biographies

Ron Mahabir, George Mason University

Ron is a PhD student with the Department of Geography and Geoinformation Science.

Kevin Gonzales, George Mason University

Kevin is a MS Geoinformatics and Geospatial student at the Department of Geography and Geoinformation Science

Jared Silk, George Mason University

Jared is a MS Geoinformatics and Geospatial student at the Department of Geography and Geoinformation Science

Downloads

Published

2015-09-28