@InProceedings{DinizMenoSchw:2018:ReInGP,
author = "Diniz, Matheus Alves and Menotti, David and Schwartz, William
Robson",
affiliation = "{Universidade Federal de Minas Gerais} and {Universidade Federal
do Paran{\'a}} and {Universidade Federal de Minas Gerais}",
title = "Face Detection at 15,000 FPS: Real-Time Inference on GPU and CPU",
booktitle = "Proceedings...",
year = "2018",
editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and
Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and
Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez,
Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de
and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa,
Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus,
Klaus de and Scheer, Sergio",
organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
address = "Porto Alegre",
keywords = "face,detection, real-time, deep learning, cpu.",
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.",
conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
conference-year = "29 Oct.-1 Nov. 2018",
language = "en",
ibi = "8JMKD3MGPAW/3S4PPHS",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3S4PPHS",
targetfile = "face_detection_at_15k_fps.pdf",
urlaccessdate = "2024, May 02"
}