Mathematical Models Using Artificial Intelligence for Surveillance Systems (eBook)

Padmesh Tripathi, Mritunjay Rai, Nitendra Kumar, Santosh Kumar (Herausgeber)

eBook Download: EPUB

2024
512 Seiten
Wiley-Scrivener (Verlag)
978-1-394-20071-9 (ISBN)

Infos

This book gives comprehensive insights into the application of AI, machine learning, and deep learning in developing efficient and optimal surveillance systems for both indoor and outdoor environments, addressing the evolving security challenges in public and private spaces.

Mathematical Models Using Artificial Intelligence for Surveillance Systems aims to collect and publish basic principles, algorithms, protocols, developing trends, and security challenges and their solutions for various indoor and outdoor surveillance applications using artificial intelligence (AI). The book addresses how AI technologies such as machine learning (ML), deep learning (DL), sensors, and other wireless devices could play a vital role in assisting various security agencies. Security and safety are the major concerns for public and private places in every country. Some places need indoor surveillance, some need outdoor surveillance, and, in some places, both are needed. The goal of this book is to provide an efficient and optimal surveillance system using AI, ML, and DL-based image processing.

The blend of machine vision technology and AI provides a more efficient surveillance system compared to traditional systems. Leading scholars and industry practitioners are expected to make significant contributions to the chapters. Their deep conversations and knowledge, which are based on references and research, will result in a wonderful book and a valuable source of information.

Padmesh Tripathi, PhD, is an associate professor of mathematics at the Indian Institute of Management and Technology College of Engineering, Greater Noida, India. He has more than 20 years of teaching experience. Additionally, he has published several research papers and book chapters in reputed journals, as well as presented papers and participated in many national and international conferences and workshops.

Mritunjay Rai is an assistant professor in the Department of Electronics and Communication at Shri Ramswaroop Memorial University, India. He has more than ten years of working experience in research and academics. Additionally, he has published many research articles in reputed journals and contributed many chapters in books, as well as reviewed many research papers in journals and national and international conferences.

Nitendra Kumar, PhD, is an assistant professor at the Indian Institute of Management and Technology College of Engineering, Greater Noida. He has more than 10 years of experience in his research areas and has published many research papers in reputed journals and six books on engineering mathematics. He contributes to the research community by volunteering to edit and has edited two books.

Santosh Kumar, PhD, is an assistant professor in the Department of Mathematics, School of Basic Sciences and Research, Sharda University, India. He has published ten research papers in the SCOPUS indexed journals, as well as two Indian patents. Dr. Kumar has published ten book chapters with reputed publishers. He has attended many national and international conferences and faculty development programs and workshops and has given many talks, and chairing sessions at both the national and international levels.

Textauszug

1
Elevating 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. By projecting pixel data into a lower-dimensional subspace, our PCA-enhanced technique preserves essential foreground information while attenuating background fluctuations. This results in more accurate object detection and reduced false alarms. Important results on diverse surveillance scenarios demonstrate the superiority of our proposed methods over conventional techniques. The adaptive GMM consistently outperforms static-rate GMMs in detecting objects under challenging lighting conditions. Similarly, the PCA-based approach showcases remarkable resilience to gradual background changes, enhancing surveillance reliability. In conclusion, this chapter contributes to the advancement of surveillance integrity by leveraging mathematical insights to improve background subtraction accuracy. Our innovative techniques, based on adaptive GMM and PCA, effectively address common limitations of traditional methods, yielding superior performance in object detection and false alarm reduction. As surveillance systems continue to play a crucial role in ensuring public safety, our research offers valuable tools to enhance their effectiveness in dynamic real-world environments.

Keywords: Mathematics, image processing, neural network, surveillance system

1.1 Introduction

The initial stage in many computer vision applications that use movies is the detection of moving objects. The background and foreground are then separated using background subtraction. The majority of them focus on the use of mathematics and machine learning models to be more resistant to the difficulties encountered in videos. The ultimate aim is for research-developed background removal techniques to be used in practical contexts, such as traffic surveillance. To identify the actual issues encountered in practise, we make an effort to conduct the most thorough survey we can on genuine applications that employed background subtraction in this context. Additionally, we pinpoint the background models that, in terms of robustness, duration, and memory needs, are employed efficiently. Segmenting stationary and moving foreground elements from a video stream is the goal of background subtraction. This activity is a key stage in many visual surveillance systems, and background removal provides an appropriate solution that delivers a decent quality-to-price tradeoff.

Here are some areas of interest and trends that have been prevalent in recent image processing literature. Particularly, moving object recognition has continued to dominate image processing research. Recent studies of Karmann et al. [1, 2] explore novel architectures, training techniques, and applications of object detection in image classification, object detection, image segmentation, and style transfer. Background subtraction has gained significant attention for its ability to generate realistic images. Recent literature [3–6] focuses on improving GAN training stability, diversity of generated samples, and applications in image synthesis, data augmentation, and super-resolution. Researchers have been working on advanced techniques like artificial intelligence in image processing. Rai et al. [7] studied the leverage AI to restore high-quality images from noisy or degraded inputs in the field of agriculture. Recently researchers [8–10] have focused on methods for land cover classification, change detection, disaster monitoring, and environmental analysis using remote sensing data using machine learning concepts. Researchers [11] have explored attention mechanisms to improve the interpretability and explainability of statistical models, particularly in tasks where understanding the model’s decision-making process is crucial. Image processing in the medical field has seen significant advancements, including automated disease detection, image registration, and analysis of medical images such as X-rays, MRI, and CT scans. Recent literature such as Dhar et al. [12] has investigated chest disease prediction tasks with an emphasis on generative models and CNN. With the demand for real-time applications, there is a focus on developing lightweight and efficient image processing algorithms suitable for resource-constrained devices like mobile phones and edge devices. The field of image processing is rapidly evolving, and new research is continuously being published. It is necessary to explore the basic mathematical concepts behind image processing.

1.2 Background Subtraction

A common technique for identifying moving objects in a series of still images from static cameras is background removal. The fundamental idea behind this method is to identify moving objects by measuring the change between the current frame and the reference frame, often known as the “background image” or “background model.” Foreground identification is the major goal of this entire technique, and it is commonly accomplished by identifying the foreground items in a video frame.

In image processing and computer vision, background subtraction is a standard method for separating foreground objects from a stationary or gradually moving backdrop. (See Figure 1.1). Applications like object tracking, surveillance, and motion detection make extensive use of it. Creating a model of the backdrop and comparing each incoming frame of the video or picture sequence with it is the underlying concept behind background subtraction. Potential foreground items are represented by the disparities between the current frame and the background model. It’s crucial to remember that although background removal is a frequently used and straightforward approach, it might not always be effective. More sophisticated algorithms (see Figure 1.2) could be more appropriate in complicated circumstances with changeable backgrounds or items that imitate the backdrop. General steps involved in this process can be seen in Figure 1.3.

Figure 1.1 Background subtraction.

Figure 1.2 Flow chart of background subtraction algorithm.

Figure 1.3 General steps of background subtraction.

1.3 Mathematics Behind Background Subtraction

Background subtraction is a fundamental technique in image processing used to separate foreground objects (such as people or vehicles) from the background in a video sequence. It’s commonly used in surveillance systems for object detection and tracking. The technique involves mathematical concepts and operations that enable the system to identify moving objects. Here’s how mathematics is used in background subtraction:

Image Representation

Each frame in a video is represented as a matrix of pixel values. For grayscale images, each pixel’s intensity is represented by a single value, while color images have multiple intensity values (e.g., red, green, blue). Mathematics is used to manipulate and analyze these pixel values.

Statistical Models

Background subtraction often utilizes statistical models to represent the background and foreground pixel distributions. One common approach is to use Gaussian distributions to model the pixel intensities of the background. Pixels that significantly deviate from this distribution are considered foreground objects.

Pixel Intensity Comparison

Mathematical calculations involve comparing pixel intensities between consecutive frames. If the difference between the current pixel intensity and the corresponding background model’s mean intensity exceeds a certain threshold, that pixel is marked as part of the foreground.

Adaptive Background Modeling

In dynamic environments, the background may change over time due to lighting variations or other factors. Adaptive background modeling uses mathematical techniques to update the background model over time, allowing it to adapt to changing conditions.

Morphological Operations

Mathematical morphology involves operations like dilation and erosion. These operations are used to remove noise,...

Produkt-Details

• Erscheint lt. Verlag: 6.8.2024
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Theorie / Studium
• Schlagworte: Advanced Surveillance System • Artificial Intelligence • Background Frame • Background Subtraction (BGS) • Convolution Neural Network • Deep learning • Foreground Frame • Gaussian mixture model (GMM) • machine learning • Support Vector Machine • Surveillance system
• ISBN-10: 1-394-20071-4 / 1394200714
• ISBN-13: 978-1-394-20071-9 / 9781394200719