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Document Type

Thesis - University Access Only

Award Date

2015

Degree Name

Master of Science (MS)

Department

Electrical Engineering and Computer Science

First Advisor

Sung Shin

Abstract

Automated vehicle detection and classification is one of many highly researched areas in Computer Science. This paper proposes and presents a hybridized method for classifying objects based on spatial orientation. Specifically, the proposed classification system explores the Histogram of Oriented Gradients feature extractor conjoined with a clustering algorithm to classify vehicle images in an unsupervised manner. HOG is a well-suited feature extractor for dense images rich with contours and edges. The pairing provides an efficacious orientation classifier for vehicle images. Training and sample data exceeded 1.8 million images and was provided by Carsforsale.com, Inc.’s vast catalogue of historical vehicle images.

Library of Congress Subject Headings

Object-oriented methods (Computer science)
Computer vision

Description

Includes bibliographical references (pages 32-35)

Format

application/pdf

Number of Pages

44

Publisher

South Dakota State University

Rights

In Copyright - Educational Use Permitted
http://rightsstatements.org/vocab/InC-EDU/1.0/

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