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Mathematical Models Using Artificial Intelligence for Surveillance Systems

Edited by Padmesh Tripathi, Mritunjay Rai, Nitendra Kumar, and Santosh Kumar
Copyright: 2024   |   Status: Published
ISBN: 9781394200733  |  Hardcover  |  
354 pages
Price: $225 USD
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One Line Description
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.

Audience
Industry professionals working in the research and development sector and the field of camera manufacturing, academics, and university students

Description
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.

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Author / Editor Details
Padmesh Tripathi, PhD completed his Ph.D. from Sharda University, Greater Noida, UP, India. Currently, Dr Tripathi is working as Professor of Mathematics in Department of AIDS at Delhi Technical Campus, Greater Noida, UP, India. He has more than 23 years of teaching experience, published 22 papers/book chapters in reputed journals/publishers and 4 Indian innovation patents. His research areas include Data Science, Machine Learning, Inverse Problems, Optimization, Signal/Image Processing, etc. Dr Tripathi has been listed in lifetime achievement by Marquis Who’s Who and received the best academician of 2021 award from SEMS Foundation, Noida, India. Dr Tripathi has been associated with several reputed publishers like IGI Global, Wiley-Scrivener, Taylor & Francis, Elsevier, Springer, Inderscience, etc. in various roles like author, reviewer, editor, guest editor, etc. Dr Tripathi received grants from prestigious institutes like Cambridge University, UK; University of California at Los Angeles, USA; INRIA, Sophia Antipolis, France; University of Eastern Finland, Kuopio, Finland; RICAM, Linz, Austria, etc and visited these places.

Mritunjay Rai, PhD has completed his Ph.D. in Thermal imaging applications in the department of Electrical Engineering from IIT-ISM Dhanbad, Master of Engineering (with distinction) in Instrumentation and Control from Birla Institute of Technology-Mesra, Ranchi, and B.Tech in ECE from Shri Ramswaroop Memorial College of Engineering and Management, Lucknow. Currently, Dr. Rai is working as Assistant Professor in Shri Ramswaroop Memorial University, Barabanki, U.P., India. Dr. Rai has more than 12 years of working experience in research as well as academics. In addition, he has guided several UG and PG projects. He has published many research articles in reputed journals published by Springer, Elsevier, IEEE, Inderscience, and MECS. He has contributed many chapters to books published by Intech Open Access, CRC, IGI Global, and Elsevier. He is an editor of books (edited) published by reputed publishers Wiley, AAP, NOVA & IGI, He is an active reviewer and has reviewed many research papers in journals and at international and national conferences. His areas of interest lie in image processing, speech processing, artificial intelligence, machine learning, deep learning, Intelligent Traffic Monitoring System, the Internet of Things (IoT), and robotics and automation.

Nitendra Kumar, PhD an accomplished scholar with a PhD in Mathematics from Sharda University and a master’s degree in mathematics and Statistics from Dr. Ram Manohar Lohia Avadh University, boasts over a decade of expertise as an Assistant Professor at Amity Business School, Amity University, Noida. His diverse research interests encompass Wavelets and its Variants, Data Mining, Inverse Problems, Epidemic Modelling, Fractional Derivatives Business Analytics, and Statistical Methods, reflecting a profound commitment to advancing knowledge across multiple domains. Dr. Kumar's prolific contributions to academia are evidenced by his extensive publication record, comprising over 30 research papers in esteemed journals, 16 book chapters, and 12 authored books on engineering mathematics, computation, and Business Analytics and related topics. Notably, his scholarly impact extends beyond traditional research avenues, as evidenced by his involvement in patenting two innovative solutions. Beyond his individual achievements, Dr. Kumar actively engages with the academic community, serving as editor for two edited books and as Guest Editor for reputable journals like the Journal of Information and Optimization Sciences, Journal of Statistical and Management Sciences, and Environment and Social Psychology journals. His editorial roles underscore his dedication to fostering intellectual discourse and shaping the trajectory of scholarly inquiry. Dr Nitendra Kumar epitomizes academic excellence, blending profound expertise with a steadfast commitment to advancing mathematical knowledge and its interdisciplinary applications.

Santosh Kumar, PhD is Assistant Professor in the Department of Mathematics, Sharda School of Basic Sciences and Research, Sharda University, Greater Noida, India. He obtained his Ph.D. degree from Aligarh Muslim University Aligarh, in 2016. He is actively involved in the research areas, namely nonlinear partial differential equations, diffusion models, wavelet transform, mathematical modeling, image processing, etc. He has taught undergraduate subjects such as linear algebra, differential equations, complex analysis, advanced calculus, and probability and statistics. He has taught real analysis, topology, functional analysis, partial differential equations, and many more at the post-graduation level. Besides attending, presenting scientific papers, delivering invited talks, and chairing sessions at national/international conferences and seminars, he has organized several workshops and conferences as an organizing secretary. He has published many research papers in reputed national and international journals and book chapters published in an edited book published by international publishers. He is also reviewer of many reputed journals.

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Table of Contents
Preface
1. Elevating Surveillance Integrity-Mathematical Insights into Background Subtraction in Image Processing
S. Priyadharsini
1.1 Introduction
1.2 Background Subtraction
1.3 Mathematics Behind Background Subtraction
1.4 Gaussian Mixture Model
1.4.1 Gaussian Mixture Model (GMM) Algorithm for Background Subtraction
1.4.2 Gaussian Mixture Model (GMM) Algorithm – A Simple Example
1.5 Principal Component Analysis
1.6 Applications
1.6.1 Military Surveillance
1.6.2 Visual Observation of Animals in Forests
1.6.3 Marine Surveillance
1.6.4 Defense Surveillance Systems
1.7 Conclusion
References
2. Machine Learning and Artificial Intelligence in the Detection of Moving Objects Using Image Processing
K. Janagi, Devarajan Balaji, P. Renuka and S. Bhuvaneswari
2.1 Introduction
2.2 Moving Object Detection
2.3 Envisaging the Object Detection
2.3.1 Filtering Algorithm
2.3.2 Identification of Object Detection in Bad Weather Circumstance
2.3.3 Color Clustering
2.3.4 Dangerous Animal Detection
2.3.5 UAV Video End-of-Line Detection and Tracking in Live Traffic
2.3.5.1 Contextual Detection
2.3.5.2 Calculation of Location of a Car
2.3.6 Estimation of Crowd
2.3.7 Parking Lot Management
2.3.8 Public Automatic Anomaly Detection Systems
2.3.9 Modification of Robust Principal Component Analysis
2.3.10 Logistics Automation
2.3.11 Detection of Criminal Behavior in Humans
2.3.12 UAV Collision Avoidance and Control System
2.3.13 An Overview of Potato Growth Stages
2.4 Conclusion
References
3. Machine Learning and Imaging-Based Vehicle Classification for Traffic Monitoring Systems
Parthiban K. and Eshan Ratnesh Srivastava
3.1 Introduction
3.2 Methods
3.2.1 Data Preparation
3.2.2 Model Training
3.2.3 Hardware and Software Configuration
3.3 Result
3.4 Conclusion
3.5 Limitations
3.6 Future Improvements
References
4. AI-Based Surveillance Systems for Effective Attendance Management: Challenges and Opportunities
Pallavi Sharda Garg, Samarth Sharma, Archana Singh and Nitendra Kumar
4.1 Introduction
4.2 Artificial Intelligence (AI) and Smart Surveillance
4.3 Artificial Intelligence (AI) and Attendance Management
4.4 Technologies in Automatic Attendance Management Image Processing
4.5 Deep Learning and Various Neural Network Techniques for Attendance Management
4.5.1 Applications of Convolutional Neural Networks (CNN) for Attendance Management
4.5.1.1 Mathematical Model of CNN
4.5.2 Applications of Recursive Neural Network (RNN) for Attendance Management
4.5.2.1 Mathematical Model of RNN
4.5.3 Applications of Generative Adversarial Network (GAN) for Attendance Management
4.5.3.1 Mathematical Model of Generalized Neural Network
4.6 Role of AI Technologies in Attendance Management
4.7 Challenges
4.8 Opportunities
4.9 Discussion & Conclusion
References
5. Enhancing Surveillance Systems through Mathematical Models and Artificial Intelligence: An Image Processing Approach
Tarun Kumar Vashishth, Vikas Sharma, Bhupendra Kumar, Kewal Krishan Sharma, Sachin Chaudhary and Rajneesh Panwar
5.1 Introduction
5.1.1 Surveillance
5.1.1.1 Crime Prevention and Detection
5.1.1.2 Public Safety
5.1.1.3 Terrorism Prevention
5.1.1.4 Traffic Management
5.1.1.5 Workplace Monitoring
5.1.1.6 Evidence Collection
5.1.1.7 Emergency Response
5.1.1.8 National Security
5.1.1.9 Public Health Monitoring
5.1.1.10 Accountability and Transparency
5.1.2 Image Processing
5.1.2.1 Image Enhancement
5.1.2.2 Image Restoration
5.1.2.3 Image Compression
5.1.2.4 Image Segmentation
5.1.2.5 Object Detection and Recognition
5.1.2.6 Image Analysis and Measurement
5.1.2.7 Image Registration
5.1.2.8 Image Classification and Machine Learning
5.1.2.9 Image Synthesis and Manipulation
5.1.2.10 Remote Sensing and Image Analysis
5.2 History of Surveillance Systems
5.3 Literature Review
5.4 Mathematical Models for Surveillance Systems
5.4.1 Overview of Mathematical Modeling in Surveillance
5.4.2 Role of Probability and Statistics in Surveillance
5.4.2.1 Anomaly Detection
5.4.2.2 Predictive Analytics
5.4.2.3 Risk Assessment
5.4.2.4 Decision Support
5.4.2.5 Data Fusion and Integration
5.4.3 Modeling Human Behavior in Surveillance Scenario
5.4.3.1 Behavioral Patterns
5.4.3.2 Machine Learning
5.4.3.3 Social Dynamics
5.4.3.4 Continuous Learning and Adaptation
5.4.3.5 Cognitive Modeling
5.4.4 Mathematical Modeling for Tracking and Motion Analysis
5.4.4.1 Object Tracking
5.4.4.2 Motion Prediction
5.4.4.3 Motion Analysis
5.4.4.4 Motion Representation
5.4.4.5 Trajectory Analysis
5.4.4.6 Data Fusion
5.4.4.7 Continuous Learning and Adaptation
5.5 Artificial Intelligence in Surveillance Systems
5.5.1 Object Recognition and Detection
5.5.2 Behavior Analysis
5.5.3 Facial Recognition
5.5.4 Video Analytics
5.5.5 Real-Time Alert Generation
5.5.6 Predictive Analytics
5.5.7 Data Management and Analytics
5.6 Use of Mathematical Models for Pre-Processing Image Data
5.6.1 Filtering and Smoothing
5.6.2 Image Enhancement
5.6.3 Edge Detection
5.6.4 Image Restoration
5.6.5 Feature Extraction
5.6.6 Dimensionality Reduction
5.7 Future Directions and Challenges
5.7.1 Deep Learning and Neural Networks
5.7.2 Real-Time Processing
5.7.3 Multi-Modal Data Fusion
5.7.4 Privacy-Preserving Techniques
5.7.5 Human-Centric Surveillance
5.7.6 Robustness to Adversarial Attacks
5.7.7 Interoperability and Scalability
5.7.8 Ethical and Legal Considerations
5.8 Conclusion
5.8.1 Summary of the Chapter
5.8.2 Key Findings and Contributions
5.8.2.1 Integration of Mathematical Models
5.8.2.2 Application of Artificial Intelligence
5.8.2.3 Future Directions
5.8.2.4 Improved Security and Public Safety
5.8.2.5 Efficiency and Automation
5.8.3 Importance of Continued Research in Enhancing Surveillance Systems
5.8.3.1 Advancements in Technology
5.8.3.2 Addressing Complex Challenges
5.8.3.3 Improving Accuracy and Efficiency
5.8.3.4 Enhancing Threat Detection and Prevention
5.8.3.5 Real-World Application and Impact
References
Key Terms
6. A Study on Object Detection Using Artificial Intelligence and Image Processing–Based Methods
Vidushi Nain, Hari Shankar Shyam, Nitendra Kumar, Padmesh Tripathi and Mritunjay Rai
6.1 Introduction
6.2 Role of Artificial Intelligence in Image Analysis
6.2.1 Object Detection and Recognition
6.2.2 Image Segmentation
6.2.3 Medical Image Analysis
6.2.4 Virtual Reality (VR) and Augmented Reality (AR)
6.3 How Artificial Intelligence Can Enhance Traditional Image Processing Algorithms and Enable New Applications
6.3.1 Image Restoration
6.3.2 Super Resolution
6.3.3 Style Transfer
6.4 Benefits of Artificial Intelligence and Image Processing Methods
6.5 Ethical Considerations Associated with AI and Image Processing
6.5.1 Privacy and the Protection of Data
6.5.2 Bias and Discrimination Artificial Intelligence (AI) Algorithms
6.5.3 Informed Approval and Transparency
6.5.4 Deep Fakes and the Spread of Misinformation
6.5.5 Trust and Safety
6.5.6 Accountability and Responsibility
6.6 Conclusion
References
7. Application of Fuzzy Approximation Method in Pattern Recognition Using Deep Learning Neural Networks and Artificial Intelligence for Surveillance
M. Geethalakshmi, Sriram V. and Vakkalagadda Drishti Rao
7.1 Introduction
7.2 Preliminaries
7.2.1 Neural Network
7.2.2 Pattern Recognition
7.2.3 Self-Organizing Maps (or Kohonen Maps)
7.2.4 Facial Recognition
7.2.5 Thumb Impression Recognition
7.3 Proposed Method
7.3.1 Mathematical Model: Pascal’s Triangle Graded Mean Approach
7.3.2 Proposed Fuzzy Approximation Method (FAM)
7.3.3 Application of FAM in Facial Recognition
7.3.4 Application of FAM in Thumb Recognition
7.3.5 Proposed Algorithm and Coding
7.4 Experimental Analysis
7.5 Proposed Solution
7.6 Application Over Facial Recognition
7.7 Application of Thumb Impression Recognition
7.8 Advantages of the Proposed Method
7.9 Conclusion
References
8. A Deep Learning System for Deep Surveillance
Aman Anand, Rajendra Kumar, Nikita Verma, Akash Bhasney and Namita Sharma
8.1 Introduction
8.2 Related Work
8.3 Method and Approach
8.3.1 Dataset Used
8.3.2 Mathematical Modelling
8.3.3 Frames Extraction and Object Detection
8.3.4 Image Pre-Processing
8.4 Model Implementations
8.4.1 SoftMax Regression
8.4.2 Support Vector Machine (SVM)
8.4.3 MatConvNet
8.4.4 CNN
8.4.5 Spatially-Sparse CNN
8.4.6 Implementation
8.5 Results and Comparative Analysis
8.6 Conclusions and Future Research Direction
References
9. Study of Traditional, Artificial Intelligence and Machine Learning Based Approaches for Moving Object Detection
Apoorv Joshi, Amrita, Rohan Sahai Mathur, Nitendra Kumar and Padmesh Tripathi
9.1 Introduction
9.2 Literature Review
9.3 Approaches for MOD
9.3.1 Traditional Approaches for MOD
9.3.1.1 Background Subtraction Methods
9.3.1.2 Optical Flow-Based Techniques
9.3.1.3 Frame Differencing and Morphological Operations
9.3.1.4 Challenges and Limitations
9.3.2 ML Approaches for MOD
9.3.2.1 Supervised Learning for Object Detection
9.3.2.2 Unsupervised Learning Approaches for Anomaly Detection
9.3.2.3 Transfer Learning and Domain Adaptation
9.3.2.4 Evaluation Metrics for ML-Based MOD
9.3.3 AI Approaches in MOD
9.3.3.1 AI-Powered Object Tracking
9.3.3.2 Reinforcement Learning for MOD
9.3.3.3 Generative Adversarial Networks in MOD
9.3.3.4 Explainable AI in MOD
9.4 Applications of AI and ML in MOD
9.5 Key Findings
9.6 Conclusion
References
10. Arduino-Based Robotic Arm for Farm Security in Rural Areas
Canute Sherwin, Shahid D. P., N. R. Hritish, Sujan Kumar S. N., Nikhil R. and K. Raju
10.1 Introduction
10.2 Literature Survey
10.3 Objectives of the Study
10.4 Significance of the Study
10.5 Working
10.6 Design of the Robotic Arm and Servo Motor Power
10.7 Fabrication
10.8 Results
10.9 Conclusion
References
11. Graph Neural Network and Imaging Based Vehicle Classification for Traffic Monitoring System
Shivam Sinha, Nilesh Kumar Singh and Lidia Ghosh
11.1 Introduction
11.2 Comprehensive Study of Vehicle Classification Technologies
11.3 Proposed Approach
11.4 Experiments and Results
11.5 Conclusion
References
12. A Novel Zone Segmentation (ZS) Method for Dynamic Obstacle Detection and Flawless Trajectory Navigation of Mobile Robot
Rapti Chaudhuri, Jashaswimalya Acharjee and Suman Deb
12.1 Introduction
12.2 Related Work
12.3 Methodology
12.3.1 Formation of Customized Drive Structure
12.3.2 Backend Construction
12.3.3 Map Representation
12.3.4 Application of Machine Learning Module for Obstacle Recognition
12.4 Evaluation
12.4.1 SLAM Map Creation and Representation
12.4.1.1 SLAM Localization
12.4.1.2 SLAM Mapping
12.4.2 ROS Rviz for Visualization
12.4.3 Loop Closure
12.4.3.1 Continuous Drift Estimation
12.4.3.2 Object Detection and Recognition
12.4.4 Dynamic Obstacle Prioritization
12.4.5 Results Obtained from SLAM
12.4.5.1 Trajectory Manipulation
12.5 Conclusion
References
13. Artificial Intelligence in Indoor or Outdoor Surveillance Systems: A Systematic View, Principles, Challenges and Applications
Varun Gupta, Tushar Bansal, Vinay Kumar Yadav and Dhrubajyoti Bhowmik
13.1 Introduction
13.2 Principles of AI-Powered Surveillance Systems
13.2.1 Object Detection
13.2.2 Face Recognition
13.2.3 License Plate Recognition
13.2.4 Anomaly Detection
13.2.5 Crowd Analysis
13.2.6 Behaviour Analysis
13.3 Machine Learning Algorithms
13.3.1 Logistic Regression
13.3.2 Support Vector Machine
13.3.3 K-Nearest Neighbour
13.3.4 Random Forest
13.3.5 Decision Tree
13.3.6 Region-Based Convolutional Neural Network (R-CNN)
13.3.7 Eigenfaces
13.3.8 Fisherfaces
13.3.9 Hidden Markov Models (HMMs)
13.3.10 Optical Character Recognition (OCR)
13.3.11 Gaussian Mixture Nodels (GMM)
13.3.12 Autoencoders
13.4 Benefits of Using AI in Surveillance Systems
13.5 Challenges of Using AI in Surveillance Systems
13.6 Conclusion
References
Index

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