Architecture for fast object detection supporting CPU-GPU hybrid and distributed computing

Yuseok Bae; Jongyoul Park

doi:10.1109/ICCE.2017.7889268

Architecture for fast object detection supporting CPU-GPU hybrid and distributed computing

Yuseok Bae, Jongyoul Park

Dept. of Applied Artificial Intelligence

Electronics and Telecommunications Research Institute

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

This paper describes architecture for fast object detection that integrates uniform local binary patterns (ULBP) with convolutional neural networks (CNN). The proposed architecture also supports CPU-GPU hybrid and distributed computing based on the Hadoop distributed computing platform considering large-scale image big data.

Original language	English
Title of host publication	2017 IEEE International Conference on Consumer Electronics, ICCE 2017
Editors	Daniel Diaz Sanchez, Jong-Hyouk Lee, Fernando Pescador
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	158-159
Number of pages	2
ISBN (Electronic)	9781509055449
DOIs	https://doi.org/10.1109/ICCE.2017.7889268
State	Published - 29 Mar 2017
Event	2017 IEEE International Conference on Consumer Electronics, ICCE 2017 - Las Vegas, United States Duration: 8 Jan 2017 → 10 Jan 2017

Publication series

Name	2017 IEEE International Conference on Consumer Electronics, ICCE 2017

Conference

Conference	2017 IEEE International Conference on Consumer Electronics, ICCE 2017
Country/Territory	United States
City	Las Vegas
Period	8/01/17 → 10/01/17

Access to Document

10.1109/ICCE.2017.7889268

Cite this

Bae, Y., & Park, J. (2017). Architecture for fast object detection supporting CPU-GPU hybrid and distributed computing. In D. D. Sanchez, J.-H. Lee, & F. Pescador (Eds.), 2017 IEEE International Conference on Consumer Electronics, ICCE 2017 (pp. 158-159). Article 7889268 (2017 IEEE International Conference on Consumer Electronics, ICCE 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE.2017.7889268

Bae, Yuseok ; Park, Jongyoul. / Architecture for fast object detection supporting CPU-GPU hybrid and distributed computing. 2017 IEEE International Conference on Consumer Electronics, ICCE 2017. editor / Daniel Diaz Sanchez ; Jong-Hyouk Lee ; Fernando Pescador. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 158-159 (2017 IEEE International Conference on Consumer Electronics, ICCE 2017).

@inproceedings{1f2d338ab6d94ccf96e4d750d3023f04,

title = "Architecture for fast object detection supporting CPU-GPU hybrid and distributed computing",

abstract = "This paper describes architecture for fast object detection that integrates uniform local binary patterns (ULBP) with convolutional neural networks (CNN). The proposed architecture also supports CPU-GPU hybrid and distributed computing based on the Hadoop distributed computing platform considering large-scale image big data.",

author = "Yuseok Bae and Jongyoul Park",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Consumer Electronics, ICCE 2017 ; Conference date: 08-01-2017 Through 10-01-2017",

year = "2017",

month = mar,

day = "29",

doi = "10.1109/ICCE.2017.7889268",

language = "English",

series = "2017 IEEE International Conference on Consumer Electronics, ICCE 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "158--159",

editor = "Sanchez, \{Daniel Diaz\} and Jong-Hyouk Lee and Fernando Pescador",

booktitle = "2017 IEEE International Conference on Consumer Electronics, ICCE 2017",

}

Bae, Y & Park, J 2017, Architecture for fast object detection supporting CPU-GPU hybrid and distributed computing. in DD Sanchez, J-H Lee & F Pescador (eds), 2017 IEEE International Conference on Consumer Electronics, ICCE 2017., 7889268, 2017 IEEE International Conference on Consumer Electronics, ICCE 2017, Institute of Electrical and Electronics Engineers Inc., pp. 158-159, 2017 IEEE International Conference on Consumer Electronics, ICCE 2017, Las Vegas, United States, 8/01/17. https://doi.org/10.1109/ICCE.2017.7889268

Architecture for fast object detection supporting CPU-GPU hybrid and distributed computing. / Bae, Yuseok; Park, Jongyoul.
2017 IEEE International Conference on Consumer Electronics, ICCE 2017. ed. / Daniel Diaz Sanchez; Jong-Hyouk Lee; Fernando Pescador. Institute of Electrical and Electronics Engineers Inc., 2017. p. 158-159 7889268 (2017 IEEE International Conference on Consumer Electronics, ICCE 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Architecture for fast object detection supporting CPU-GPU hybrid and distributed computing

AU - Bae, Yuseok

AU - Park, Jongyoul

PY - 2017/3/29

Y1 - 2017/3/29

N2 - This paper describes architecture for fast object detection that integrates uniform local binary patterns (ULBP) with convolutional neural networks (CNN). The proposed architecture also supports CPU-GPU hybrid and distributed computing based on the Hadoop distributed computing platform considering large-scale image big data.

AB - This paper describes architecture for fast object detection that integrates uniform local binary patterns (ULBP) with convolutional neural networks (CNN). The proposed architecture also supports CPU-GPU hybrid and distributed computing based on the Hadoop distributed computing platform considering large-scale image big data.

UR - http://www.scopus.com/inward/record.url?scp=85018270317&partnerID=8YFLogxK

U2 - 10.1109/ICCE.2017.7889268

DO - 10.1109/ICCE.2017.7889268

M3 - Conference contribution

AN - SCOPUS:85018270317

T3 - 2017 IEEE International Conference on Consumer Electronics, ICCE 2017

SP - 158

EP - 159

BT - 2017 IEEE International Conference on Consumer Electronics, ICCE 2017

A2 - Sanchez, Daniel Diaz

A2 - Lee, Jong-Hyouk

A2 - Pescador, Fernando

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 IEEE International Conference on Consumer Electronics, ICCE 2017

Y2 - 8 January 2017 through 10 January 2017

ER -

Bae Y, Park J. Architecture for fast object detection supporting CPU-GPU hybrid and distributed computing. In Sanchez DD, Lee JH, Pescador F, editors, 2017 IEEE International Conference on Consumer Electronics, ICCE 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 158-159. 7889268. (2017 IEEE International Conference on Consumer Electronics, ICCE 2017). doi: 10.1109/ICCE.2017.7889268

Architecture for fast object detection supporting CPU-GPU hybrid and distributed computing

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this