2016 IEEE National Aerospace and Electronics Conference (NAECON) and Ohio Innovation Summit (OIS) 2016
DOI: 10.1109/naecon.2016.7856842
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GPU-accelerated feature tracking

Abstract: Graves, Alex. M.S., Department of Computer Science and Engineering, Wright State University, 2016. GPU-Accelerated Feature Tracking.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 fr… Show more

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Cited by 3 publications
(1 citation statement)
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“…Thus, the SIFTGPU is widely used in various computer tasks including SFM, simultaneous localization and mapping (SLAM), and robotic navigation. Inspired by SIFTGPU, Graves et al [49] developed KLTGPU routines using OpenCL, then resulting in a 92% reduction in runtime compared to a CPU-based implementation. Cao et al [50] proposed a GPU-accelerated feature tracking (GFT) method for SFM-based 3D reconstruction, which has a 20 times Appl.…”
Section: Feature Trackingmentioning
confidence: 99%
“…Thus, the SIFTGPU is widely used in various computer tasks including SFM, simultaneous localization and mapping (SLAM), and robotic navigation. Inspired by SIFTGPU, Graves et al [49] developed KLTGPU routines using OpenCL, then resulting in a 92% reduction in runtime compared to a CPU-based implementation. Cao et al [50] proposed a GPU-accelerated feature tracking (GFT) method for SFM-based 3D reconstruction, which has a 20 times Appl.…”
Section: Feature Trackingmentioning
confidence: 99%