Continuity of object tracking
Date
2022
Authors
Williams, Haney W., author
Simske, Steven J., advisor
Azimi-Sadjadi, Mahmood R., committee member
Chong, Edwin K. P., committee member
Beveridge, J. Ross, committee member
Journal Title
Journal ISSN
Volume Title
Abstract
The demand for object tracking (OT) applications has been increasing for the past few decades in many areas of interest: security, surveillance, intelligence gathering, and reconnaissance. Lately, newly-defined requirements for unmanned vehicles have enhanced the interest in OT. Advancements in machine learning, data analytics, and deep learning have facilitated the recognition and tracking of objects of interest; however, continuous tracking is currently a problem of interest to many research projects. This dissertation presents a system implementing a means to continuously track an object and predict its trajectory based on its previous pathway, even when the object is partially or fully concealed for a period of time. The system is divided into two phases: The first phase exploits a single fixed camera system and the second phase is composed of a mesh of multiple fixed cameras. The first phase system is composed of six main subsystems: Image Processing, Detection Algorithm, Image Subtractor, Image Tracking, Tracking Predictor, and the Feedback Analyzer. The second phase of the system adds two main subsystems: Coordination Manager and Camera Controller Manager. Combined, these systems allow for reasonable object continuity in the face of object concealment.
Description
Rights Access
Subject
continuous tracking
correlation of camera mesh network
systems engineering design methodology
convolutional neural network
collaborative learning
object recognition