A Hybrid Approach for Moving Object Detection Using TV-L1 Features and RPCA–MOG
International journal of intelligent engineering and systems · 2022
Robust Principal Component Analysis (RPCA) has recently been active everywhere for dimension reduction in image processing, display and pattern recognition.Methods based on low-rank sparse representations, which make some specific significant assumptions, have recently received a lot of attention in background modelling.Meanwhile, a powerful analysis framework is needed to handle background areas or foreground motion at various scales.this paper presents a hybrid approach along with total variation L1 (TV-L1) features and reproductive RPCA model in low rank background subtraction modelling and sparse matrix with mixture of Gaussians (MoG) as foreground modelling.The hybrid structure with TV-L1 features imposes a hierarchical RPCA on the singular values of the low-rank component and MOG sparsity indicators.The proposed work was evaluated on the CDnet2014 (ChangeDetection.net)dataset, obtained result as accuracy was 92.9%, 86.7% 95.7% for Highway, Escalator, and Indoor respectively.The proposed method is compared with traditional methods and obtained relative reconstruction error is 0.01529 as a lower side.