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		<identifier>8JMKD3MGPAW/3S4PPHS</identifier>
		<repository>sid.inpe.br/sibgrapi/2018/10.26.02.37</repository>
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		<citationkey>DinizMenoSchw:2018:ReInGP</citationkey>
		<title>Face Detection at 15,000 FPS: Real-Time Inference on GPU and CPU</title>
		<format>On-line</format>
		<year>2018</year>
		<date>Oct. 29 - Nov. 1, 2018</date>
		<numberoffiles>1</numberoffiles>
		<size>5923 KiB</size>
		<author>Diniz, Matheus Alves,</author>
		<author>Menotti, David,</author>
		<author>Schwartz, William Robson,</author>
		<affiliation>Universidade Federal de Minas Gerais</affiliation>
		<affiliation>Universidade Federal do Paraná</affiliation>
		<affiliation>Universidade Federal de Minas Gerais</affiliation>
		<editor>Ross, Arun,</editor>
		<editor>Gastal, Eduardo S. L.,</editor>
		<editor>Jorge, Joaquim A.,</editor>
		<editor>Queiroz, Ricardo L. de,</editor>
		<editor>Minetto, Rodrigo,</editor>
		<editor>Sarkar, Sudeep,</editor>
		<editor>Papa, João Paulo,</editor>
		<editor>Oliveira, Manuel M.,</editor>
		<editor>Arbeláez, Pablo,</editor>
		<editor>Mery, Domingo,</editor>
		<editor>Oliveira, Maria Cristina Ferreira de,</editor>
		<editor>Spina, Thiago Vallin,</editor>
		<editor>Mendes, Caroline Mazetto,</editor>
		<editor>Costa, Henrique Sérgio Gutierrez,</editor>
		<editor>Mejail, Marta Estela,</editor>
		<editor>Geus, Klaus de,</editor>
		<editor>Scheer, Sergio,</editor>
		<e-mailaddress>matheusad95@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 31 (SIBGRAPI)</conferencename>
		<conferencelocation>Foz do Iguaçu, PR, Brazil</conferencelocation>
		<booktitle>Proceedings</booktitle>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<documentstage>not transferred</documentstage>
		<transferableflag>1</transferableflag>
		<tertiarytype>Undergraduate Work</tertiarytype>
		<keywords>face,detection, real-time, deep learning, cpu.</keywords>
		<abstract>Object  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.</abstract>
		<language>en</language>
		<targetfile>face_detection_at_15k_fps.pdf</targetfile>
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		<url>http://sibgrapi.sid.inpe.br/rep-/sid.inpe.br/sibgrapi/2018/10.26.02.37</url>
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