Demo: Distributed Real-Time Generative 3D Hand Tracking using Edge GPGPU Acceleration

Ammar Qammaz; Sokol Kosta; Nikolaos Kyriazis; Antonis Argyros

doi:10.1145/3210240.3211112

Demo: Distributed Real-Time Generative 3D Hand Tracking using Edge GPGPU Acceleration

Ammar Qammaz, Sokol Kosta, Nikolaos Kyriazis, Antonis Argyros

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

Abstract

This work demonstrates a real-time 3D hand tracking application that runs via computation offloading. The proposed framework enables the application to run on low-end mobile devices such as laptops and tablets, despite the fact that they lack the sufficient hardware to perform the required computations locally. The network connection takes the place of a GPGPU accelerator and sharing resources with a larger workstation becomes the acceleration mechanism. The unique properties of a generative optimizer are examined and constitute a challenging use-case, since the requirement for real-time performance makes it very latency-sensitive.

Original language	English
Title of host publication	MobiSys '18 - International Conference on Mobile Systems, Applications and Services
Number of pages	1
Place of Publication	Munich, Germany
Publisher	Association for Computing Machinery
Publication date	10 Jun 2018
Pages	540-540
ISBN (Print)	978-1-4503-5720-3
ISBN (Electronic)	9781450357203
DOIs	https://doi.org/10.1145/3210240.3211112
Publication status	Published - 10 Jun 2018
Event	16th ACM International Conference on Mobile Systems, Applications, and Services - Munich, Germany Duration: 10 Jun 2018 → 15 Jun 2018 https://www.sigmobile.org/mobisys/2018/

Conference

Conference	16th ACM International Conference on Mobile Systems, Applications, and Services
Country/Territory	Germany
City	Munich
Period	10/06/2018 → 15/06/2018
Internet address	https://www.sigmobile.org/mobisys/2018/

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10.1145/3210240.3211112

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@inproceedings{52fb4831f37c43d8b171d859910cfbde,

title = "Demo: Distributed Real-Time Generative 3D Hand Tracking using Edge GPGPU Acceleration",

abstract = "This work demonstrates a real-time 3D hand tracking application that runs via computation offloading. The proposed framework enables the application to run on low-end mobile devices such as laptops and tablets, despite the fact that they lack the sufficient hardware to perform the required computations locally. The network connection takes the place of a GPGPU accelerator and sharing resources with a larger workstation becomes the acceleration mechanism. The unique properties of a generative optimizer are examined and constitute a challenging use-case, since the requirement for real-time performance makes it very latency-sensitive.",

author = "Ammar Qammaz and Sokol Kosta and Nikolaos Kyriazis and Antonis Argyros",

year = "2018",

month = jun,

day = "10",

doi = "10.1145/3210240.3211112",

language = "English",

isbn = "978-1-4503-5720-3",

pages = "540--540",

booktitle = "MobiSys '18 - International Conference on Mobile Systems, Applications and Services",

publisher = "Association for Computing Machinery",

address = "United States",

note = "16th ACM International Conference on Mobile Systems, Applications, and Services, MobiSys ; Conference date: 10-06-2018 Through 15-06-2018",

url = "https://www.sigmobile.org/mobisys/2018/",

}

Qammaz, A, Kosta, S, Kyriazis, N & Argyros, A 2018, Demo: Distributed Real-Time Generative 3D Hand Tracking using Edge GPGPU Acceleration. in MobiSys '18 - International Conference on Mobile Systems, Applications and Services. Association for Computing Machinery, Munich, Germany, pp. 540-540, 16th ACM International Conference on Mobile Systems, Applications, and Services, Munich, Germany, 10/06/2018. https://doi.org/10.1145/3210240.3211112

Demo: Distributed Real-Time Generative 3D Hand Tracking using Edge GPGPU Acceleration. / Qammaz, Ammar; Kosta, Sokol; Kyriazis, Nikolaos et al.
MobiSys '18 - International Conference on Mobile Systems, Applications and Services. Munich, Germany: Association for Computing Machinery, 2018. p. 540-540.

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

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T2 - 16th ACM International Conference on Mobile Systems, Applications, and Services

AU - Qammaz, Ammar

AU - Kosta, Sokol

AU - Kyriazis, Nikolaos

AU - Argyros, Antonis

PY - 2018/6/10

Y1 - 2018/6/10

N2 - This work demonstrates a real-time 3D hand tracking application that runs via computation offloading. The proposed framework enables the application to run on low-end mobile devices such as laptops and tablets, despite the fact that they lack the sufficient hardware to perform the required computations locally. The network connection takes the place of a GPGPU accelerator and sharing resources with a larger workstation becomes the acceleration mechanism. The unique properties of a generative optimizer are examined and constitute a challenging use-case, since the requirement for real-time performance makes it very latency-sensitive.

AB - This work demonstrates a real-time 3D hand tracking application that runs via computation offloading. The proposed framework enables the application to run on low-end mobile devices such as laptops and tablets, despite the fact that they lack the sufficient hardware to perform the required computations locally. The network connection takes the place of a GPGPU accelerator and sharing resources with a larger workstation becomes the acceleration mechanism. The unique properties of a generative optimizer are examined and constitute a challenging use-case, since the requirement for real-time performance makes it very latency-sensitive.

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U2 - 10.1145/3210240.3211112

DO - 10.1145/3210240.3211112

M3 - Article in proceeding

SN - 978-1-4503-5720-3

SP - 540

EP - 540

BT - MobiSys '18 - International Conference on Mobile Systems, Applications and Services

PB - Association for Computing Machinery

CY - Munich, Germany

Y2 - 10 June 2018 through 15 June 2018

ER -

Demo: Distributed Real-Time Generative 3D Hand Tracking using Edge GPGPU Acceleration

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