Title

Adaptive Background Modeling with Temporal Feature Update for Dynamic Foreground Object Removal

Author

Li Yin, Utah State UniversityFollow

Date of Award:

12-2016

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Electrical and Computer Engineering

Advisor/Chair:

Jacob Gunther

Abstract

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.

Checksum

230490643384cc2ea6dfc485c5f62792

Recommended Citation

Yin, Li, "Adaptive Background Modeling with Temporal Feature Update for Dynamic Foreground Object Removal" (2016). All Graduate Theses and Dissertations. 5040.
https://digitalcommons.usu.edu/etd/5040