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

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.

Author dc:creator

• McDowell, William

Contributors dc:contributor

• Mikhael, Wasfy

• Intelligent transportation systems
• tracking
• kalman filters
• classification
• Electrical and Computer Engineering
• Electrical and Electronics
• Engineering
• Dissertations, Academic -- Engineering and Computer Science; Engineering and Computer Science -- Dissertations, Academic

Language dc:language

English

Identifier

CFE0005976

OAI identifier oai:identifier

oai:stars.library.ucf.edu:etd-2386