國立虎尾科技大學 |

Realtime Image Processing for Resource Constrained Devices.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Realtime Image Processing for Resource Constrained Devices./
作者:	Streiffer, Christopher.
面頁冊數:	1 online resource (98 pages)
附註:	Source: Masters Abstracts International, Volume: 57-05.
Contained By:	Masters Abstracts International57-05(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355953824

Realtime Image Processing for Resource Constrained Devices.
Streiffer, Christopher.

Realtime Image Processing for Resource Constrained Devices. - 1 online resource (98 pages)

Source: Masters Abstracts International, Volume: 57-05.

Thesis (M.S.)--Duke University, 2018.

Includes bibliographical references

With the proliferation of embedded sensors within smartphone and Internet-of-Things devices, applications have programmatic access to more data processing than ever before. At the same time, advances in computer vision and deep learning have fostered methodology for performing complex, yet powerful operations on spatial and temporal data. Capitalizing on this union, applications are capable of providing advanced functionality to their users through features such as augmented reality and image classification. However, the devices responsible for running these libraries often lack the sufficient hardware to replicate the parallelization and straight-line speed of high-end servers. For image processing applications, this means that realtime performance is difficult without compromising functionality.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355953824Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Realtime Image Processing for Resource Constrained Devices.
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500 $a Source: Masters Abstracts International, Volume: 57-05.
500 $a Adviser: Landon Cox.
502 $a Thesis (M.S.)--Duke University, 2018.
504 $a Includes bibliographical references
520 $a With the proliferation of embedded sensors within smartphone and Internet-of-Things devices, applications have programmatic access to more data processing than ever before. At the same time, advances in computer vision and deep learning have fostered methodology for performing complex, yet powerful operations on spatial and temporal data. Capitalizing on this union, applications are capable of providing advanced functionality to their users through features such as augmented reality and image classification. However, the devices responsible for running these libraries often lack the sufficient hardware to replicate the parallelization and straight-line speed of high-end servers. For image processing applications, this means that realtime performance is difficult without compromising functionality.
520 $a To detail this emerging paradigm, this work presents and examines two image processing applications which offer advanced functionality. The first, DarNet, utilizes the TensorFlow library to perform distracted driving classification based on image data using a Convolutional Neural Network (CNN). The second, PrivateEye, uses the OpenCV library to provide a camera based access-control privacy framework for Android users. While this advanced processing allows for enhanced functionality, the computationally expensive operations impose limitations on the realtime performance of these applications due to the lack of sufficient hardware.
520 $a This work posits that realtime image processing applications running on resource constrained devices require the external use of edge servers. To this extent, this work presents ePrivateEye, an extension to PrivateEye which provides code offloading to an edge server. The results of this work shows that offloading video-frame analysis to the edge at a metro-scale distance allows ePrivateEye to analyze more frames than PrivateEye's local processing over the same period, and achieve realtime performance of 30 fps with perfect precision and negligible impact on energy efficiency.
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538 $a Mode of access: World Wide Web
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