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Reference TypeConference Proceedings
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPAW/3S4PPHS
Repositorysid.inpe.br/sibgrapi/2018/10.26.02.37
Last Update2018:10.26.02.37.27 matheusad95@gmail.com
Metadatasid.inpe.br/sibgrapi/2018/10.26.02.37.27
Metadata Last Update2020:02.20.22.06.51 administrator
Citation KeyDinizMenoSchw:2018:ReInGP
TitleFace Detection at 15,000 FPS: Real-Time Inference on GPU and CPU
FormatOn-line
Year2018
DateOct. 29 - Nov. 1, 2018
Access Date2020, Nov. 29
Number of Files1
Size5923 KiB
Context area
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
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
History2018-10-26 02:37:27 :: matheusad95@gmail.com -> administrator ::
2020-02-20 22:06:51 :: administrator -> :: 2018
Content and structure area
Is the master or a copy?is the master
Document Stagecompleted
Document Stagenot transferred
Transferable1
Tertiary TypeUndergraduate Work
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.
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Languageen
Target Fileface_detection_at_15k_fps.pdf
User Groupmatheusad95@gmail.com
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Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3RPADUS
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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