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Reseach Article

Parallelization and Optimization of Pedestrian Detection Software on NVIDIA GPGPU using CUDA-C

Published on May 2016 by A.d. Londhe, K.v. Bhosale, Sayli Zope, Roshani Rode, Rasika Waichal, Rajat Toshniwal
National Conference on Advancements in Computer & Information Technology
Foundation of Computer Science USA
NCACIT2016 - Number 3
May 2016
Authors: A.d. Londhe, K.v. Bhosale, Sayli Zope, Roshani Rode, Rasika Waichal, Rajat Toshniwal
09b37a3c-55a4-49b3-9401-95a2c4cd6a0b

A.d. Londhe, K.v. Bhosale, Sayli Zope, Roshani Rode, Rasika Waichal, Rajat Toshniwal . Parallelization and Optimization of Pedestrian Detection Software on NVIDIA GPGPU using CUDA-C. National Conference on Advancements in Computer & Information Technology. NCACIT2016, 3 (May 2016), 18-20.

@article{
author = { A.d. Londhe, K.v. Bhosale, Sayli Zope, Roshani Rode, Rasika Waichal, Rajat Toshniwal },
title = { Parallelization and Optimization of Pedestrian Detection Software on NVIDIA GPGPU using CUDA-C },
journal = { National Conference on Advancements in Computer & Information Technology },
issue_date = { May 2016 },
volume = { NCACIT2016 },
number = { 3 },
month = { May },
year = { 2016 },
issn = 0975-8887,
pages = { 18-20 },
numpages = 3,
url = { /proceedings/ncacit2016/number3/24713-3051/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Advancements in Computer & Information Technology
%A A.d. Londhe
%A K.v. Bhosale
%A Sayli Zope
%A Roshani Rode
%A Rasika Waichal
%A Rajat Toshniwal
%T Parallelization and Optimization of Pedestrian Detection Software on NVIDIA GPGPU using CUDA-C
%J National Conference on Advancements in Computer & Information Technology
%@ 0975-8887
%V NCACIT2016
%N 3
%P 18-20
%D 2016
%I International Journal of Computer Applications
Abstract

The future of the computation is the Graphical Processing Unit, i. e. the GPU. The graphics cards have been shown in the pasture of image processing and accelerated interpretation of 3D scenes, and computational capability that these GPUs acquire, they are rising into immense parallel computing units. It is quite simple to program any graphics processor to execute universal parallel tasks. But after understanding the various architectural features of the graphics processor, it can be used to execute other demanding tasks as well. In this paper, the use CUDA (Compute Unified Device Architecture) can fully utilize most tremendous power of these GPUs. CUDA is most popular parallel computing architecture of NVIDIA. It enables dramatic increases in computing performance, by harnessing the power of the GPU. In this, the sequential software of the famous image processing problem, the pedestrian detection[1] and parallelize it to run on GPUs and increase the performance of that software. We have presented a new pedestrian detector that improves in speed and quality of the pedestrian detection. By efficiently handling different scales and transferring computation from the test time to training time, detection speed is improved.

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Index Terms

Computer Science
Information Sciences

Keywords

Pedestrians Detection Compute Unified Device Architecture(cuda) Object Detection Graphics Processing Units (gpus) Parallel Architectures svm Classifier.