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GPU-Accelerated Feature Tracking.pdf (1.2 MB)
ETD Abstract Container
Abstract Header
GPU-Accelerated Feature Tracking
Author Info
Graves, Alex
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=wright1462372516
Abstract Details
Year and Degree
2016, Master of Science (MS), Wright State University, Computer Science.
Abstract
The motivation of this research is to prove that GPUs can provide significant speedup of long-executing image processing algorithms by way of parallelization and massive data throughput. This thesis accelerates the well-known KLT feature tracking algorithm using OpenCL and an NVidia GeForce GTX 780 GPU. KLT is a fast, efficient and accurate feature tracker but can easily suffer from low frame rates when tracking many features in an HD video sequence. This research explains how KLT could benefit from GPGPU programming and provides the corresponding OpenCL implementation. Additionally, various optimization techniques are emphasized to further boost GPU performance. The experiments conducted prove that when tracking over 500 features in an HD dataset, GPU-based KLT provides a 92% reduction in total runtime compared to a CPU-based implementation. Furthermore, the experiments demonstrate that these features are tracked while maintaining similar accuracy to the CPU results.
Committee
Thomas Wischgoll, Ph.D (Advisor)
Arthur Goshtasby, Ph.D (Committee Member)
Krishnaprasad Thirunarayan, Ph.D (Committee Member)
Pages
65 p.
Subject Headings
Computer Science
Keywords
KLT, OpenCL, Computer Vision
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Citations
Graves, A. (2016).
GPU-Accelerated Feature Tracking
[Master's thesis, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1462372516
APA Style (7th edition)
Graves, Alex.
GPU-Accelerated Feature Tracking.
2016. Wright State University, Master's thesis.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=wright1462372516.
MLA Style (8th edition)
Graves, Alex. "GPU-Accelerated Feature Tracking." Master's thesis, Wright State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1462372516
Chicago Manual of Style (17th edition)
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Document number:
wright1462372516
Download Count:
2,299
Copyright Info
© 2016, all rights reserved.
This open access ETD is published by Wright State University and OhioLINK.