As the popularity of short-form videos booms, more and more people are using the technique of video inpainting to edit their videos. The goal of Video Inpainting is to fill in missing regions of a given video sequence with contents that are both spatially and temporally coherent. Yet, before we learn how to code video inpainting algorithms, we need to know how to extract the background in a frame. The Singular-Value Decomposition (SVD) is one of the most efficient ways to remove pedestrians in a frame and return a clean background frame as a result. Moreover, SVD can also support data scientists in lessening the dataset’s complexity before starting any training by reducing the dimension of the dataset to handle complex data analysis efficiently.

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After completing this guided project you will be able to:

Everything to do this project will be provided by the Skills Network Labs. This project uses Python and Jupyter notebook to process the matrices, and we recommend that you review the concepts of matrix and PCA before starting. But you should be able to complete this project even if you are not very familiar with these concepts.