Video stabilization is an important aspect of video processing, especially for handheld devices where unintended camera movement can significantly degrade the resulting recording. This paper investigates four image based methods for video stabilization. The study explores the Lukas-Kanade, Inverse Compositional Lukas-Kanade, Farnebäck Optical Flow, and GMFlow methods, evaluating their sub-pixel accuracy, real-time performance, and robustness to in-frame motion such as a person walking in front of the camera. The results indicate that while all methods achieve sub-pixel precision, real-time execution on a mobile phone is not feasible with the current implementations. Furthermore, the methods exhibit varying levels of difficulty in handling in-frame motion, with RANSAC-based approaches partially compensating for non-camera-induced movement. The paper also discusses the potential of machine learning techniques, represented by GMFlow, in enhancing stabilization quality at the cost of computational complexity. The findings offer valuable insights for the development of more efficient and robust video stabilization solutions.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-528623 |
| Date | January 2024 |
| Creators | Zetterberg, Zackeus |
| Publisher | Uppsala universitet, Avdelningen Vi3 |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | UPTEC F, 1401-5757 ; 24014 |
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