The goal of this intelligent transportation systems work is to improve the understanding of the impact of carbon emissions caused by vehicular traffic on highway systems. In order to achieve this goal, this work implements a pipeline for vehicle segmentation, feature extraction, and classification using the existing Virginia Department of Transportation (VDOT) infrastructure on networked traffic cameras. The VDOT traffic video is analyzed for vehicle detection and segmentation using an adaptive Gaussian mixture model algorithm. The morphological properties and histogram of oriented features are derived from the detected and segmented vehicles. Finally, vehicle classification is performed using a multiclass support vector machine classifier. The resulting classification scheme offers an average classification rate of 86% under good quality segmentation. The segmented vehicle and classification data can be used to obtain estimation of carbon emissions.

Date Published: 19 September 2014
PDF: 10 pages
Proc. SPIE 9216, Optics and Photonics for Information Processing VIII, 92160K (19 September 2014); doi: 10.1117/12.2063323

Published in SPIE Proceedings Vol. 9216:
Optics and Photonics for Information Processing VIII
Abdul A. S. Awwal; Khan M. Iftekharuddin; Mohammad A. Matin; Andrés Márquez, Editor(s)