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Vehicle Tracking and Classification via 3D Geometries for Intelligent Transportation Systems

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Date Issued:
2015
Abstract/Description:
In this dissertation, we present generalized techniques which allow for the tracking and classification of vehicles by tracking various Point(s) of Interest (PoI) on a vehicle. Tracking the various PoI allows for the composition of those points into 3D geometries which are unique to a given vehicle type. We demonstrate this technique using passive, simulated image based sensor measurements and three separate inertial track formulations. We demonstrate the capability to classify the 3D geometries in multiple transform domains (PCA (&) LDA) using Minimum Euclidean Distance, Maximum Likelihood and Artificial Neural Networks. Additionally, we demonstrate the ability to fuse separate classifiers from multiple domains via Bayesian Networks to achieve ensemble classification.
Title: Vehicle Tracking and Classification via 3D Geometries for Intelligent Transportation Systems.
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Name(s): Mcdowell, William, Author
Mikhael, Wasfy, Committee Chair
Jones, W Linwood, Committee Member
Haralambous, Michael, Committee Member
Atia, George, Committee Member
Mahalanobis, Abhijit, Committee Member
Muise, Robert, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2015
Publisher: University of Central Florida
Language(s): English
Abstract/Description: In this dissertation, we present generalized techniques which allow for the tracking and classification of vehicles by tracking various Point(s) of Interest (PoI) on a vehicle. Tracking the various PoI allows for the composition of those points into 3D geometries which are unique to a given vehicle type. We demonstrate this technique using passive, simulated image based sensor measurements and three separate inertial track formulations. We demonstrate the capability to classify the 3D geometries in multiple transform domains (PCA (&) LDA) using Minimum Euclidean Distance, Maximum Likelihood and Artificial Neural Networks. Additionally, we demonstrate the ability to fuse separate classifiers from multiple domains via Bayesian Networks to achieve ensemble classification.
Identifier: CFE0005976 (IID), ucf:50790 (fedora)
Note(s): 2015-12-01
Ph.D.
Engineering and Computer Science, Electrical Engineering and Computer Engineering
Doctoral
This record was generated from author submitted information.
Subject(s): Intelligent Transportation Systems -- Tracking -- Kalman Filters -- Classification
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0005976
Restrictions on Access: public 2015-12-15
Host Institution: UCF

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