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Applications of Compressive Sensing To Surveillance Problems

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Date Issued:
2012
Abstract/Description:
In many surveillance scenarios, one concern that arises is how to construct an imager that is capable of capturing the scene with high fidelity. This could be problematic for two reasons: first, the optics and electronics in the camera may have difficulty in dealing with so much information; secondly, bandwidth constraints, may pose difficulty in transmitting information from the imager to the user efficiently for reconstruction or realization. In this thesis, we will discuss a mathematical framework that is capable of skirting the two aforementioned issues. This framework is rooted in a technique commonly referred to as compressive sensing. We will explore two of the seminal works in compressive sensing and will present the key theorems and definitions from these two papers. We will then survey three different surveillance scenarios and their respective compressive sensing solutions. The original contribution of this thesis is the development of a distributed compressive sensing model.
Title: Applications of Compressive Sensing To Surveillance Problems.
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Name(s): Huff, Christopher, Author
Mohapatra, Ram, Committee Chair
Sun, Qiyu, Committee CoChair
Han, Deguang, Committee Member
, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2012
Publisher: University of Central Florida
Language(s): English
Abstract/Description: In many surveillance scenarios, one concern that arises is how to construct an imager that is capable of capturing the scene with high fidelity. This could be problematic for two reasons: first, the optics and electronics in the camera may have difficulty in dealing with so much information; secondly, bandwidth constraints, may pose difficulty in transmitting information from the imager to the user efficiently for reconstruction or realization. In this thesis, we will discuss a mathematical framework that is capable of skirting the two aforementioned issues. This framework is rooted in a technique commonly referred to as compressive sensing. We will explore two of the seminal works in compressive sensing and will present the key theorems and definitions from these two papers. We will then survey three different surveillance scenarios and their respective compressive sensing solutions. The original contribution of this thesis is the development of a distributed compressive sensing model.
Identifier: CFE0004317 (IID), ucf:49473 (fedora)
Note(s): 2012-05-01
M.S.
Sciences, Mathematics
Masters
This record was generated from author submitted information.
Subject(s): compressive sensing -- difference images -- motion detection -- surveillance
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0004317
Restrictions on Access: public 2012-05-15
Host Institution: UCF

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