Predicting multiple target tracking performance for applications on video sequences

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.ii ACKNOWLEDGEMENTS

Target Tracking Based on Base Station Node Using Prediction Method and Cluster Structure in Wireless Sensor Networks

“…In other words, the node leaves the tracking of the target as soon as that target entries to its neighbors' region. The fourth strategy is dynamic clustering [18]. In this model, the nodes are divided into two categories, the nodes with the highenergy and low-energy.…”

“…Fig.4. A view of the antenna used in the proposed protocol [18] Sensors activated in the field receive a clustering message and extract the information and then the clustering phase is completed.…”