In the study of computer vision, background modeling is a fundamental and critical task in many conventional applications. This thesis presents an introduction to background modeling and various computer vision techniques for estimating the background model to achieve the goal of removing dynamic objects in a video sequence.
The process of estimating the background model with temporal changes in the absence of foreground moving objects is called adaptive background modeling. In this thesis, three adaptive background modeling approaches were presented for the purpose of developing \teacher removal" algorithms. First, an adaptive background modeling algorithm based on linear adaptive prediction is presented. Second, an adaptive background modeling algorithm based on statistical dispersion is presented. Third, a novel adaptive background modeling algorithm based on low rank and sparsity constraints is presented. The design and implementation of these algorithms are discussed in detail, and the experimental results produced by each algorithm are presented. Lastly, the results of this research are generalized and potential future research is discussed.
| Identifer | oai:union.ndltd.org:UTAHS/oai:digitalcommons.usu.edu:etd-6078 |
| Date | 01 December 2016 |
| Creators | Yin, Li |
| Publisher | DigitalCommons@USU |
| Source Sets | Utah State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | All Graduate Theses and Dissertations |
| Rights | Copyright for this work is held by the author. Transmission or reproduction of materials protected by copyright beyond that allowed by fair use requires the written permission of the copyright owners. Works not in the public domain cannot be commercially exploited without permission of the copyright owner. Responsibility for any use rests exclusively with the user. For more information contact Andrew Wesolek (andrew.wesolek@usu.edu). |
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