How to Estimate Global Motion Non-Iteratively From a Coarsely Sampled Motion Vector Field
| Autor: | Junggi Lee, Kyeongbo Kong, Seung-Jun Shin, Woo-Jin Song |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Object (computer science) Motion vector Motion (physics) Domain (mathematical analysis) Motion vector field Motion estimation Content (measure theory) Outlier 0202 electrical engineering electronic engineering information engineering Media Technology 020201 artificial intelligence & image processing Electrical and Electronic Engineering Algorithm |
| Zdroj: | IEEE Transactions on Circuits and Systems for Video Technology. 29:3729-3742 |
| ISSN: | 1558-2205 1051-8215 |
| DOI: | 10.1109/tcsvt.2018.2882513 |
| Popis: | Nowadays, as the amount of video content increases, the importance of global motion estimation is increasing more and more. This paper considers motion estimation within a compressed domain and presents a robust non-iterative global motion estimation algorithm that efficiently eliminates outliers. The said algorithm is comprised of two parts. The first part uses the distinct-outlier mask based on statistical analysis to eliminate the distinct outliers and uses the current-object mask based on the reference-object mask to eliminate the object outliers. In the second part, the proposed median error designed to be robust to the outlier ratio has been used to estimate weights with higher values as the motion vector gets closer to global motion. Using these weights, the proposed algorithm can accurately estimate global motion non-iteratively, although the outlier ratio may change. The simulation results demonstrate that the proposed algorithm demonstrates the highest estimation accuracy and processing speed in synthetic motion fields as well as real video sequences. |
| Databáze: | OpenAIRE |
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