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%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2018/10.26.02.37
%2 sid.inpe.br/sibgrapi/2018/10.26.02.37.27
%A Diniz, Matheus Alves,
%A Menotti, David,
%A Schwartz, William Robson,
%@affiliation Universidade Federal de Minas Gerais
%@affiliation Universidade Federal do Paraná
%@affiliation Universidade Federal de Minas Gerais
%T Face Detection at 15,000 FPS: Real-Time Inference on GPU and CPU
%B Conference on Graphics, Patterns and Images, 31 (SIBGRAPI)
%D 2018
%E Ross, Arun,
%E Gastal, Eduardo S. L.,
%E Jorge, Joaquim A.,
%E Queiroz, Ricardo L. de,
%E Minetto, Rodrigo,
%E Sarkar, Sudeep,
%E Papa, João Paulo,
%E Oliveira, Manuel M.,
%E Arbeláez, Pablo,
%E Mery, Domingo,
%E Oliveira, Maria Cristina Ferreira de,
%E Spina, Thiago Vallin,
%E Mendes, Caroline Mazetto,
%E Costa, Henrique Sérgio Gutierrez,
%E Mejail, Marta Estela,
%E Geus, Klaus de,
%E Scheer, Sergio,
%S Proceedings
%8 Oct. 29 - Nov. 1, 2018
%J Porto Alegre
%I Sociedade Brasileira de Computação
%C Foz do Iguaçu, PR, Brazil
%K face,detection, real-time, deep learning, cpu.
%X 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.
%@language en
%3 face_detection_at_15k_fps.pdf


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