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http://hdl.handle.net/20.500.12188/33951| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Stameski, Kristijan | en_US |
| dc.contributor.author | Gusev, Marjan | en_US |
| dc.date.accessioned | 2025-08-25T08:50:20Z | - |
| dc.date.available | 2025-08-25T08:50:20Z | - |
| dc.date.issued | 2024-11-26 | - |
| dc.identifier.uri | http://hdl.handle.net/20.500.12188/33951 | - |
| dc.description.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. | en_US |
| dc.publisher | IEEE | en_US |
| dc.subject | Lucas-Kanade optical flow , Gaussian pyramid , flow field , GPU (Graphics Processing Unit) , CPU (Central Processing Unit) , CUDA | en_US |
| dc.title | Speeding up Dense Optical Flow Estimation with CUDA | en_US |
| dc.type | Proceedings | en_US |
| dc.relation.conference | 2024 32nd Telecommunications Forum (TELFOR) | en_US |
| item.grantfulltext | none | - |
| item.fulltext | No Fulltext | - |
| Appears in Collections: | Faculty of Computer Science and Engineering: Conference papers | |
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