Date of Award
Summer 2016
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Electrical and Computer Engineering
First Advisor
Bishop, Robert H.
Second Advisor
Medeiros, Henry
Third Advisor
Povinelli, Richard
Fourth Advisor
Spiller, Elaine
Abstract
This dissertation presents a framework to predict the performance of multiple target tracking (MTT) techniques. The framework is based on the mathematical descriptors of point processes, the probability generating functional (p.g.fl). It is shown that conceptually the p.g.fls of MTT techniques can be interpreted as a transform that can be marginalized to an expression that encodes all the information regarding the likelihood model as well as the underlying assumptions present in a given tracking technique. In order to use this approach for tracker performance prediction in video sequences, a framework that combines video quality assessment concepts and the marginalized transform is introduced. The multiple hypothesis tracker (MHT), Joint Probabilistic Data Association (JPDA), Markov Chain Monte Carlo (MCMC) data association, and the Probability Hypothesis Density filter (PHD) are used as a test cases. We introduce their transforms and perform a numerical comparison to predict their performance under identical conditions. We also introduce the concepts that present the base for estimation in general and for applications in computer vision.