Publication Date

Spring 2016

Degree Type

Master's Project

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Chris Tseng

Second Advisor

Tsau Young Lin

Third Advisor

Duc Thanh Tran

Keywords

image feature tracking classification

Abstract

Pattern recognition is a field of machine learning with applications to areas such as text recognition and computer vision. Machine learning algorithms, such as convolutional neural networks, may be trained to classify images. However, such tasks may be computationally intensive for a commercial computer for larger volumes or larger sizes of images. Cloud computing allows one to overcome the processing and memory constraints of average commercial computers, allowing computations on larger amounts of data. In this project, we developed a system for detection and tracking of moving human and vehicle objects in videos in real time or near real time. We trained various classifiers to identify objects of interest as either vehicular or human. We then compared the accuracy of different machine learning algorithms, and we compared the training runtime between a commercial computer and a virtual machine on the cloud.

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