Detecting the Foreground Dynamic Scenes using Gaussian Mixture Model Analysis Compared with Novel Principal Component Analysis

Many computer vision applications rely on object recognition in video streams, and the most common technique for doing so is called foreground subtraction. updated backdrop model with a new frame. However, precise object identification is challenging in static and dynamic foregrounds. This study proposes a novel approach. Recently, Novel Principal Component Analysis, also known as (NPCA), has been used extensively across the industry for dimension reduction in image processing, presentation, and pattern identification to this issue that is based on the Gaussian Mixture Model (GMM) technique. A new updating method has been implemented to achieve a more stable model for the dynamic zones. This study provides a comprehensive overview and evaluation of other subtraction methods. The experimental results demonstrate the superiority of the proposed method over numerous top-tier object detection methods.