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Gpu-Accelerated Feature Tracking, Alex Graves
Gpu-Accelerated Feature Tracking, Alex Graves
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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 …
A Hybrid Approach To Aerial Video Image Registration, Karol T. Salva
A Hybrid Approach To Aerial Video Image Registration, Karol T. Salva
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Many video processing applications, such as motion detection and tracking, rely on accurate and robust alignment between consecutive video frames. Traditional approaches to video image registration, such as pyramidal Kanade-Lucas-Tomasi (KLT) feature detection and tracking are fast and subpixel accurate, but are not robust to large inter-frame displacements due to rotation, scale, or translation. This thesis presents an alternative hybrid approach using normalized gradient correlation (NGC) in the frequency domain and normalized cross-correlation (NCC) in the spatial domain that is fast, accurate, and robust to large displacements. A scale space search is incorporated into NGC to enable more consistent recovery …