2. Repository of UKIM
  3. Faculty of Computer Science and Engineering
  4. Faculty of Computer Science and Engineering: Conference papers

Repository of UKIM


Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/33951
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dc.contributor.authorStameski, Kristijanen_US
dc.contributor.authorGusev, Marjanen_US
dc.date.accessioned2025-08-25T08:50:20Z-
dc.date.available2025-08-25T08:50:20Z-
dc.date.issued2024-11-26-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/33951-
dc.description.abstractOptical 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.en_US
dc.publisherIEEEen_US
dc.subjectLucas-Kanade optical flow , Gaussian pyramid , flow field , GPU (Graphics Processing Unit) , CPU (Central Processing Unit) , CUDAen_US
dc.titleSpeeding up Dense Optical Flow Estimation with CUDAen_US
dc.typeProceedingsen_US
dc.relation.conference2024 32nd Telecommunications Forum (TELFOR)en_US
item.fulltextNo Fulltext-
item.grantfulltextnone-
Appears in Collections:Faculty of Computer Science and Engineering: Conference papers
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