Speeding up Viola–Jones Algorithm using Multi–Core GPU Implementation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F13%3APU104507" target="_blank" >RIV/00216305:26220/13:PU104507 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/TSP.2013.6614050" target="_blank" >http://dx.doi.org/10.1109/TSP.2013.6614050</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TSP.2013.6614050" target="_blank" >10.1109/TSP.2013.6614050</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Speeding up Viola–Jones Algorithm using Multi–Core GPU Implementation

Popis výsledku v původním jazyce

Graphic Processing Units (GPUs) offer cheap and high-performance computation capabilities by offloading compute-intensive portions of the application to the GPU, while the remainder of the code still runs on a CPU. This paper introduces an multi–GPU CUDA implementation of training of object detection using Viola–Jones algorithm that has accelerated of two the most time consuming operations in training process by using two dual–core NVIDIA GeForce GTX 690. When compared to single thread implementation on Intel Core i7 3770 with 3.7GHz frequency, the first accelerated part of training process was speeded up 151 times and the second accelerated part was speeded up 124 times using two dual–core GPUs. This paper examines overall computational time of the Viola–Jones training process with the use of: one core CPU, one GPU, two GPUs, 3GPUs and 4GPUs. Trained detector was applied on testing set containing real world images.

Název v anglickém jazyce

Speeding up Viola–Jones Algorithm using Multi–Core GPU Implementation

Popis výsledku anglicky

Graphic Processing Units (GPUs) offer cheap and high-performance computation capabilities by offloading compute-intensive portions of the application to the GPU, while the remainder of the code still runs on a CPU. This paper introduces an multi–GPU CUDA implementation of training of object detection using Viola–Jones algorithm that has accelerated of two the most time consuming operations in training process by using two dual–core NVIDIA GeForce GTX 690. When compared to single thread implementation on Intel Core i7 3770 with 3.7GHz frequency, the first accelerated part of training process was speeded up 151 times and the second accelerated part was speeded up 124 times using two dual–core GPUs. This paper examines overall computational time of the Viola–Jones training process with the use of: one core CPU, one GPU, two GPUs, 3GPUs and 4GPUs. Trained detector was applied on testing set containing real world images.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/FR-TI4%2F151" target="_blank" >FR-TI4/151: Výzkum a vývoj technologie pro detekci emocí v nestrukturovaných datech</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

36th International Conference on Telecommunications and Signal processing

ISBN

978-1-4799-0402-0

ISSN

—

e-ISSN

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Počet stran výsledku

5

Strana od-do

808-812

Název nakladatele

Neuveden

Místo vydání

Neuveden

Místo konání akce

Rome

Datum konání akce

2. 7. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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