Speeding up Dense Optical Flow Estimation with CUDA
Date Issued
2024-11-26
Author(s)
Stameski, Kristijan
Gusev, Marjan
Abstract
Optical flow is the perceived movement of a pixel within the video. It is inherently helpful for motion tracking and may also be used as a preprocessing for other computer vision algorithms or machine learning. Algorithmic optical flow estimation is slow and resource-intensive, but real-time performance can be achieved by using GPUs. This paper discusses implementing and optimizing a pyramidal Lucas-Kanade optical flow algorithm in CUDA.
Subjects