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Smart Surveillance: Spatial Tracking...

Bollig, Charles.

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Smart Surveillance: Spatial Tracking With Computer Vision and Machine Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Smart Surveillance: Spatial Tracking With Computer Vision and Machine Learning./
作者:	Bollig, Charles.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	92 p.
附註:	Source: Masters Abstracts International, Volume: 80-07.
Contained By:	Masters Abstracts International80-07.
標題:	Geographic information science. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13425134
ISBN:	9780438785724

Smart Surveillance: Spatial Tracking With Computer Vision and Machine Learning.
Bollig, Charles.

Smart Surveillance: Spatial Tracking With Computer Vision and Machine Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 92 p.

Source: Masters Abstracts International, Volume: 80-07.

Thesis (M.S.)--University of Colorado at Denver, 2018.

This item must not be added to any third party search indexes.

Historically, video surveillance has heavily relied on a person (or people) sitting behind an array of monitors, alert for any kind of suspicious behavior. However, this method of monitoring requires constant vigilance, stamina, and exacting attention to detail to be effective as a means of security. Deep learning has made "out of the box" computer vision technologies accessible to those with little more than a basic computer science background. This has allowed engineers and scientists to adapt these easily accessible computer vision (along with simple machine learning) tools into "smart visual surveillance" systems. This research explores the viability of a spatial tracking surveillance system utilizing fundamental components of these extremely accessible tools. Using generalized linear regression models, we translate object-detection camera positions into real-world locations. In configuring the spatial tracking system and training the regression models, we explore several methods of real world mapping, including GPS and manually measured distances. Suitability of the models are assessed using four evaluation methods: 'Leave One Out,' 'Train-to-Test,' 'Path Approximation,' and 'Moving Target.' Several trials in different locations are conducted to assess varying conditions, as well as the development and repeat-ability of the configuration process. In addition, we design and build a web-based application as a proof of concept for the user-facing spatial tracking surveillance system.

ISBN: 9780438785724Subjects--Topical Terms:

3432445
Geographic information science.

Smart Surveillance: Spatial Tracking With Computer Vision and Machine Learning.
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