國立虎尾科技大學 |

GPU Implementation of Video Analytics Algorithms for Aerial Imaging.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	GPU Implementation of Video Analytics Algorithms for Aerial Imaging./
作者:	Teters, Evan.
面頁冊數:	1 online resource (84 pages)
附註:	Source: Masters Abstracts International, Volume: 85-02.
Contained By:	Masters Abstracts International85-02.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9798380151115

GPU Implementation of Video Analytics Algorithms for Aerial Imaging.
Teters, Evan.

GPU Implementation of Video Analytics Algorithms for Aerial Imaging. - 1 online resource (84 pages)

Source: Masters Abstracts International, Volume: 85-02.

Thesis (M.S.)--University of Missouri - Columbia, 2023.

Includes bibliographical references

This work examines several algorithms that together make up parts of an image processing pipeline called Video Mosaicing and Summarization (VMZ). This pipeline takes as input geospatial or biomedical videos and produces large stitched-together frames (mosaics) of the video's subject.The content of these videos presents numerous challenges, such as poor lighting and a rapidly changing scene. The algorithms of VMZ were chosen carefully to address these challenges.With the output of VMZ, numerous tasks can be done. Stabilized imagery allows for easier object tracking, and the mosaics allow a quick understanding of the scene. These use-cases with aerial imagery are even more valuable when considered from the edge, where they can be applied as a drone is collecting the data. When executing video analytics algorithms, one of the most important metrics for real-life use is performance. All the accuracy in the world does not guarantee usefulness if the algorithms cannot provide that accuracy in a timely and actionable manner. Thus the goal of this work is to explore means and tools to implement video analytics algorithms, particularly the ones that make up the VMZ pipeline, on GPU devices-making them faster and more available for real-time use. This work presents four algorithms that have been converted to make use of the GPU in the GStreamer environment on NVIDIA GPUs. With GStreamer these algorithms are easily modular and lend themselves well to experimentation and real-life use even in pipelines beyond VMZ.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380151115Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Aerial imageryIndex Terms--Genre/Form:

554714
Electronic books.

GPU Implementation of Video Analytics Algorithms for Aerial Imaging.
LDR:02862ntm a22003977 4500 001 1143591
005 20240517104606.5
006 m o d
007 cr mn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798380151115
035 $a (MiAaPQ)AAI30487281
035 $a AAI30487281
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Teters, Evan. $3 1468324
245 1 0 $a GPU Implementation of Video Analytics Algorithms for Aerial Imaging.
264 0 $c 2023
300 $a 1 online resource (84 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 85-02.
500 $a Advisor: Palaniappan, Kannappan.
502 $a Thesis (M.S.)--University of Missouri - Columbia, 2023.
504 $a Includes bibliographical references
520 $a This work examines several algorithms that together make up parts of an image processing pipeline called Video Mosaicing and Summarization (VMZ). This pipeline takes as input geospatial or biomedical videos and produces large stitched-together frames (mosaics) of the video's subject.The content of these videos presents numerous challenges, such as poor lighting and a rapidly changing scene. The algorithms of VMZ were chosen carefully to address these challenges.With the output of VMZ, numerous tasks can be done. Stabilized imagery allows for easier object tracking, and the mosaics allow a quick understanding of the scene. These use-cases with aerial imagery are even more valuable when considered from the edge, where they can be applied as a drone is collecting the data. When executing video analytics algorithms, one of the most important metrics for real-life use is performance. All the accuracy in the world does not guarantee usefulness if the algorithms cannot provide that accuracy in a timely and actionable manner. Thus the goal of this work is to explore means and tools to implement video analytics algorithms, particularly the ones that make up the VMZ pipeline, on GPU devices-making them faster and more available for real-time use. This work presents four algorithms that have been converted to make use of the GPU in the GStreamer environment on NVIDIA GPUs. With GStreamer these algorithms are easily modular and lend themselves well to experimentation and real-life use even in pipelines beyond VMZ.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Remote sensing. $3 557272
650 4 $a Information technology. $3 559429
653 $a Aerial imagery
653 $a Embedded device
653 $a GPU
653 $a GStreamer
653 $a Video analytics
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0489
690 $a 0799
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Missouri - Columbia. $b Computer Science. $3 1466732
773 0 $t Masters Abstracts International $g 85-02.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30487281 $z click for full text (PQDT)