Mathematical Models Using Artificial Intelligence for Surveillance Systems

1Elevating Surveillance Integrity-Mathematical Insights into Background Subtraction in Image Processing

S. Priyadharsini

Department of Mathematics, Sri Krishna Arts and Science College, Coimbatore, India

Abstract

The ever-increasing demand for surveillance and security systems necessitates robust and reliable image processing techniques. Among these, background subtraction plays a pivotal role in detecting moving objects and activities in dynamic environments. This paper presents a comprehensive exploration of background subtraction methods with a focus on elevating surveillance integrity through mathematical insights. Traditional background subtraction techniques often struggle with varying lighting conditions, shadows, and noise, leading to false positives and negatives. To address these challenges, our study delves into advanced mathematical models that enhance the accuracy and robustness of background subtraction algorithms. We propose a novel approach that integrates Gaussian Mixture Models (GMM) with adaptive learning rates. By dynamically adjusting the learning rates based on pixel intensity variations, our method adapts to changing environments and improves the differentiation between foreground and background. This adaptive GMM offers a finely tuned balance between sensitivity and specificity, crucial for surveillance applications. Furthermore, we introduce a novel method based on Principal Component Analysis (PCA) to mitigate the impact of dynamic background changes. ...