Background Subtraction Using A Hybrid Modelling Based Technique
Saumya Maurya, Mahipal Singh Choudhry · 2021
In recent years, Background Subtraction (BGS) has become a major topic of study in the field of Computer Vision for performing moving object detection. BGS is typically utilized in video surveillance cameras to monitor moving objects. To accomplish the aforementioned job, this study provides a new method based on Hybrid modelling technique that combines two models, namely Robust Principal Component Analysis (RPCA) using Principal Component Pursuit (PCP) and randomized Singular Value Decomposition (rSVD). Video sequence is decomposed into frames and a data matrix is created with these frames as a column vector and the above-mentioned methods are then applied on the data matrix to achieve the backdrop and foreground of the video. Experiments are run on two well-known datasets, CDnet (2014) and BMC, as well as a random YouTube video sequence. In comparison to the classic PCP model, experimental results reveal that the proposed model has lower computing complexity and produces results in less time.