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Document Type

Thesis - University Access Only

Award Date


Degree Name

Master of Science (MS)

Department / School

Electrical Engineering and Computer Science

First Advisor

Sung Shin


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, Inc.’s vast catalogue of historical vehicle images.

Library of Congress Subject Headings

Object-oriented methods (Computer science)
Computer vision


Includes bibliographical references (pages 32-35)



Number of Pages



South Dakota State University



Rights Statement

In Copyright