Date of Award

Fall 2014

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Electrical & Computer Engineering

Program/Concentration

Electrical and Computer Engineering

Committee Director

Khan M. Iftekharuttdin

Committee Member

Jiang Li

Committee Member

Mecit Cetin

Call Number for Print

Special Collections LD4331.E55 F655 2014

Abstract

The goal of this intelligent transportation systems work is to develop a computer vision method that is view angle independent for segmenting and classifying vehicular traffic on highway systems. In order to achieve this goal, this work implements an algorithm 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. Speed estimation is performed using a single camera calibration. Size and shape features from morphological properties and texture features from histogram of oriented gradients are derived from the detected vehicles. Finally, vehicle classification is performed using a multiclass support vector machine classifier, with handling for multiple vehicle detections through an iterative over segmentation process. The resulting algorithm is considered for the quality of vehicle segmentation, vehicle classification accuracy, and a timing analysis for suitability as a real-time application.

Rights

In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

DOI

10.25777/ewwv-yp78

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