Dissertation/Thesis Abstract

Parallelization of lane detection algorithm using OpenCL
by Kalay, Harshal A., M.S., California State University, Long Beach, 2015, 106; 1603755
Abstract (Summary)

In this thesis we explore and experiment on using OpenCL for the parallelization of an important computer vision problem of lane detection. Lane detection aims at identifying lane markings on a road and has applications in autonomous vehicles as well as for providing guidance to the drivers. The parallelization is implemented using OpenCL on Graphics Processing Units (GPUs) as well as on multi-core CPU, both these platforms are supported by OpenCL library for parallel programming.

Our study aims at finding an effective way to parallelize the lane detection using OpenCL through experimentations. Lane detection involves use of image processing algorithms and computer vision techniques, which are both often parallelizable and may benefit greatly by using OpenCL. With our hardware configuration, we are able to achieve eight, four and six times the speedup on multi-core CPU, PCIe based GPU and CPU integrated GPU systems respectively, when compared to sequential C++ program.

Indexing (document details)
Advisor: Lam, Shui
Commitee: Hoffman, Michael, Maples, Tracy
School: California State University, Long Beach
Department: Computer Engineering and Computer Science
School Location: United States -- California
Source: MAI 55/02M(E), Masters Abstracts International
Subjects: Computer science
Keywords: GPU, OpenCL, Parallelization
Publication Number: 1603755
ISBN: 978-1-339-23512-7
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