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 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.iv

Superpixel-Based Feature Tracking for Structure from Motion

“…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.…”