東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Optimizing Large-Scale Camera Analytic Systems with Wireless Insights.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Optimizing Large-Scale Camera Analytic Systems with Wireless Insights./
作者:	Hu, Pan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	114 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-05, Section: A.
Contained By:	Dissertations Abstracts International83-05A.
標題:	Wireless networks. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28688397
ISBN:	9798544204305

Optimizing Large-Scale Camera Analytic Systems with Wireless Insights.
Hu, Pan.

Optimizing Large-Scale Camera Analytic Systems with Wireless Insights. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 114 p.

Source: Dissertations Abstracts International, Volume: 83-05, Section: A.

Thesis (Ph.D.)--Stanford University, 2021.

This item must not be sold to any third party vendors.

Cameras are key enablers for a wide range of IoT use cases including smart cities, intelligent transportation, AI-enabled farms, and more. Many of them are connected to the Internet via wireless technologies like LPWAN(LoRaWAN, SigFox) and 5G. Conventional camera analytics systems treat the underlying network as a pipe, ignoring the fact that wireless links are lossy and wireless is a broadcast medium.First, I present Starfish that is inspired by the lossy link insight. Starfish is a resilient image compression framework design for LPWAN that processes all the information loss in the application layer, thus simplifying the overall system design. To our best knowledge, it is the first DNN-based image compression framework that runs efficiently on low-cost AIoT hardware. It could be adapted to different task objectives and datasets, and exhibits graceful degradation in lossy scenarios without the need to design and tune source/channel coding. It uses neural network architecture search to generate DNN configuration thus reducing human labor and enable generalization to diverse AIoT hardware. Such end-toend design reuses the DNN inference pipeline as in object detection/image classification tasks thus it could be easily adopted on various DNN accelerators.Next, I present YOUO (You Only Upload Once) that leverages the broadcast insight. YOUO is an object re-identification system that shifts computation from cloud to devices for better privacy and lower network/computation cost. Each target object may have been captured multiple times by multiple cameras in large-scale camera networks. With the help of multicasting service provided in 5G eMBMS, YOUO enables efficient collaboration across cameras, thus significantly reduced network traffic by more than two orders of magnitude based on experiment results on a large-scale vehicle re-identification dataset. YOUO is 3x as efficient in bandwidth when compared with optimal single-camera filtering with minimal accuracy loss while achieving 70%+ accuracy when uploading only once per object (the absolute minimum) for the entire camera network.

ISBN: 9798544204305Subjects--Topical Terms:

1531264
Wireless networks.

Optimizing Large-Scale Camera Analytic Systems with Wireless Insights.
LDR:03241nmm a2200361 4500 001 2344875
005 20220531062203.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798544204305
035 $a (MiAaPQ)AAI28688397
035 $a (MiAaPQ)STANFORDtq397gn7659
035 $a AAI28688397
040 $a MiAaPQ $c MiAaPQ
100 1 $a Hu, Pan. $3 3195107
245 1 0 $a Optimizing Large-Scale Camera Analytic Systems with Wireless Insights.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 114 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-05, Section: A.
500 $a Advisor: Katti, Sachin; Asgar, Zain; Prabhakar, Balaji.
502 $a Thesis (Ph.D.)--Stanford University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Cameras are key enablers for a wide range of IoT use cases including smart cities, intelligent transportation, AI-enabled farms, and more. Many of them are connected to the Internet via wireless technologies like LPWAN(LoRaWAN, SigFox) and 5G. Conventional camera analytics systems treat the underlying network as a pipe, ignoring the fact that wireless links are lossy and wireless is a broadcast medium.First, I present Starfish that is inspired by the lossy link insight. Starfish is a resilient image compression framework design for LPWAN that processes all the information loss in the application layer, thus simplifying the overall system design. To our best knowledge, it is the first DNN-based image compression framework that runs efficiently on low-cost AIoT hardware. It could be adapted to different task objectives and datasets, and exhibits graceful degradation in lossy scenarios without the need to design and tune source/channel coding. It uses neural network architecture search to generate DNN configuration thus reducing human labor and enable generalization to diverse AIoT hardware. Such end-toend design reuses the DNN inference pipeline as in object detection/image classification tasks thus it could be easily adopted on various DNN accelerators.Next, I present YOUO (You Only Upload Once) that leverages the broadcast insight. YOUO is an object re-identification system that shifts computation from cloud to devices for better privacy and lower network/computation cost. Each target object may have been captured multiple times by multiple cameras in large-scale camera networks. With the help of multicasting service provided in 5G eMBMS, YOUO enables efficient collaboration across cameras, thus significantly reduced network traffic by more than two orders of magnitude based on experiment results on a large-scale vehicle re-identification dataset. YOUO is 3x as efficient in bandwidth when compared with optimal single-camera filtering with minimal accuracy loss while achieving 70%+ accuracy when uploading only once per object (the absolute minimum) for the entire camera network.
590 $a School code: 0212.
650 4 $a Wireless networks. $3 1531264
650 4 $a Data integrity. $3 2142314
650 4 $a Cameras. $3 524039
650 4 $a Wide area networks. $3 3683705
650 4 $a Collaboration. $3 3556296
650 4 $a Power. $3 518736
650 4 $a Bandwidths. $3 3560998
650 4 $a Artificial intelligence. $3 516317
650 4 $a Systems design. $3 3433840
650 4 $a Optimization. $3 891104
650 4 $a Neural networks. $3 677449
650 4 $a Energy efficiency. $3 3555643
650 4 $a Energy consumption. $3 631630
650 4 $a Visualization. $3 586179
650 4 $a Traffic congestion. $3 706812
650 4 $a Design. $3 518875
650 4 $a Electrical engineering. $3 649834
650 4 $a Energy. $3 876794
650 4 $a Engineering. $3 586835
650 4 $a Transportation. $3 555912
690 $a 0800
690 $a 0389
690 $a 0544
690 $a 0791
690 $a 0537
690 $a 0709
710 2 $a Stanford University. $3 754827
773 0 $t Dissertations Abstracts International $g 83-05A.
790 $a 0212
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28688397