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<p>Preface xv</p> <p>1 Elevating Surveillance Integrity-Mathematical Insights into Background Subtraction in Image Processing 1<br /><i>S. Priyadharsini</i></p> <p>2 Machine Learning and Artificial Intelligence in the Detection of Moving Objects Using Image Processing 19<br /><i>K. Janagi, Devarajan Balaji, P. Renuka and S. Bhuvaneswari</i></p> <p>3 Machine Learning and Imaging-Based Vehicle Classification for Traffic Monitoring Systems 51<br /><i>Parthiban K. and Eshan Ratnesh Srivastava</i></p> <p>4 AI-Based Surveillance Systems for Effective Attendance Management: Challenges and Opportunities 69<br /><i>Pallavi Sharda Garg, Samarth Sharma, Archana Singh and Nitendra Kumar</i></p> <p>5 Enhancing Surveillance Systems through Mathematical Models and Artificial Intelligence: An Image Processing Approach 91<br /><i>Tarun Kumar Vashishth, Vikas Sharma, Bhupendra Kumar, Kewal Krishan Sharma, Sachin Chaudhary and Rajneesh Panwar</i></p> <p>6 A Study on Object Detection Using Artificial Intelligence and Image Processing—Based Methods 121<br /><i>Vidushi Nain, Hari Shankar Shyam, Nitendra Kumar, Padmesh Tripathi and Mritunjay Rai</i></p> <p>7 Application of Fuzzy Approximation Method in Pattern Recognition Using Deep Learning Neural Networks and Artificial Intelligence for Surveillance 149<br /><i>M. Geethalakshmi, Sriram V. and Vakkalagadda Drishti Rao</i></p> <p>8 A Deep Learning System for Deep Surveillance 169<br /><i>Aman Anand, Rajendra Kumar, Nikita Verma, Akash Bhasney and Namita Sharma</i></p> <p>9 Study of Traditional, Artificial Intelligence and Machine Learning Based Approaches for Moving Object Detection 187<br /><i>Apoorv Joshi, Amrita, Rohan Sahai Mathur, Nitendra Kumar and Padmesh Tripathi</i></p> <p>10 Arduino-Based Robotic Arm for Farm Security in Rural Areas 215<br /><i>Canute Sherwin, Shahid D. P., N. R. Hritish, Sujan Kumar S. N., Nikhil R. and K. Raju</i></p> <p>11 Graph Neural Network and Imaging Based Vehicle Classification for Traffic Monitoring System 241<br /><i>Shivam Sinha, Nilesh kumar Singh and Lidia Ghosh</i></p> <p>12 A Novel Zone Segmentation (ZS) Method for Dynamic Obstacle Detection and Flawless Trajectory Navigation of Mobile Robot 271<br /><i>Rapti Chaudhuri, Jashaswimalya Acharjee and Suman Deb</i></p> <p>13 Artificial Intelligence in Indoor or Outdoor Surveillance Systems: A Systematic View, Principles, Challenges and Applications 293<br /><i>Varun Gupta, Tushar Bansal, Vinay Kumar Yadav and Dhrubajyoti Bhowmik</i></p> <p>References 330</p> <p>Index 335</p>

<p><b>Padmesh Tripathi, PhD,</b> 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. <p><b>Mritunjay Rai</b> 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. <p><b>Nitendra Kumar, PhD,</b> 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. <p><b>Santosh Kumar, PhD,</b> 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.

<p><b>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.</b> <p><i>Mathematical Models Using Artificial Intelligence for Surveillance Systems</i> 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. <p>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.

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