Continuity of object tracking
| dc.contributor.author | Williams, Haney W., author | |
| dc.contributor.author | Simske, Steven J., advisor | |
| dc.contributor.author | Azimi-Sadjadi, Mahmood R., committee member | |
| dc.contributor.author | Chong, Edwin K. P., committee member | |
| dc.contributor.author | Beveridge, J. Ross, committee member | |
| dc.date.accessioned | 2022-05-30T10:22:37Z | |
| dc.date.available | 2022-05-30T10:22:37Z | |
| dc.date.issued | 2022 | |
| dc.description.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. | |
| dc.format.medium | born digital | |
| dc.format.medium | doctoral dissertations | |
| dc.identifier | Williams_colostate_0053A_17080.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/235298 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2020- | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.subject | continuous tracking | |
| dc.subject | correlation of camera mesh network | |
| dc.subject | systems engineering design methodology | |
| dc.subject | convolutional neural network | |
| dc.subject | collaborative learning | |
| dc.subject | object recognition | |
| dc.title | Continuity of object tracking | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Systems Engineering | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy (Ph.D.) |
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