Foreground Detection in Dynamic Scenes using Singular Value Decomposition Algorithm in Comparison with Gaussian Mixture Model to measure F-score (Journal of Pharmaceutical Negative Results, 2022)
Aim: The purpose of this work is to represent a foreground detection method using a novel Singular Value Decomposition (SVD) algorithm that gives improved accuracy and F-score. Materials and Methods: ClinCalc is a tool used tocompute sample sizesand displays the results of sample analyses. Cdnet 2014 dataset demonstrates our foreground detection using novel Singular value decomposition algorithm. Here Singular value decomposition is compared with the Gaussian mixture model(GMM). Results: Poor detection of moving objects is improved by using Singular value decomposition with mean F-score rate of 83% and accuracy of87%. Conclusion: Singular value decomposition subtracts the background and detects the moving objects with better accuracy and F-score compared with GMM.