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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3S4PPHS
Repositorysid.inpe.br/sibgrapi/2018/10.26.02.37
Last Update2018:10.26.02.37.27 (UTC) matheusad95@gmail.com
Metadata Repositorysid.inpe.br/sibgrapi/2018/10.26.02.37.27
Metadata Last Update2022:05.18.22.18.35 (UTC) administrator
Citation KeyDinizMenoSchw:2018:ReInGP
TitleFace Detection at 15,000 FPS: Real-Time Inference on GPU and CPU
FormatOn-line
Year2018
Access Date2024, May 02
Number of Files1
Size5923 KiB
2. Context
Author1 Diniz, Matheus Alves
2 Menotti, David
3 Schwartz, William Robson
Affiliation1 Universidade Federal de Minas Gerais
2 Universidade Federal do Paraná
3 Universidade Federal de Minas Gerais
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
e-Mail Addressmatheusad95@gmail.com
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Date29 Oct.-1 Nov. 2018
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeUndergraduate Work
History (UTC)2018-10-26 02:37:27 :: matheusad95@gmail.com -> administrator ::
2022-05-18 22:18:35 :: administrator -> :: 2018
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordsface
detection
real-time
deep learning
cpu
AbstractObject detection is a key task in computer vision since it is the first step in the pipeline of many applications such as person re-identification, vehicle identification, and face verification. Recently, the best performing object detectors have been achieved with deep learning and one common characteristic among them is that they are a very slow on ordinary hardware. Reported real time object detectors are usually measured with high-end GPUs, which is inappropriate for scenarios where energy efficiency and low costs are required. We were able to train a very light face detection architecture by greatly reducing the number of parameters and input size of a convolutional network. Our model is capable of performing inference in real time on a hardware as simple as a Raspberry Pi. Furthermore, when evaluated on a GPU, we were able to achieve up to 15,000 frames per second.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2018 > Face Detection at...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3S4PPHS
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3S4PPHS
Languageen
Target Fileface_detection_at_15k_fps.pdf
User Groupmatheusad95@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3RPADUS
Citing Item Listsid.inpe.br/sibgrapi/2018/09.03.20.37 13
sid.inpe.br/banon/2001/03.30.15.38.24 1
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume


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