Computational Intelligence in Surveillance Systems Using Image Processing

Jay Kumar Pandey, Mritunjay Rai, Momina Shaheen, Faizan Ahmad

• Publisher: Elsevier
• Year: 2026
• eBook ISBN: 9780443364099
• Topic: Computer Science
• Subtopic: Computer Vision & Pattern Recognition
• Index: Computer Science

Traditional surveillance systems struggle to process large volumes of visual data, identify specific objects or behaviors, and adapt to dynamic environments. Computational intelligence, which encompasses techniques like artificial intelligence (AI), machine learning (ML), and computer vision, offers powerful tools to address these challenges by enabling automated analysis, pattern recognition, and decision-making based on visual data. Computational Intelligence in Surveillance Systems Using Image Processing addresses the unique challenges and ethical considerations of applying AI and ML, offering a nuanced understanding of the regulatory landscape. It provides insights into the responsible development and deployment of technologies to unlock the transformative potential of computational intelligence to revolutionize surveillance systems and advance the capabilities of security and monitoring across various sectors. - Discusses emerging trends, potential challenges, and areas for future research, providing a roadmap for scholars looking to contribute to the evolving field of image processing - Explains how AI and ML algorithms can be applied to analyze and interpret visual data captured by surveillance cameras - Considers the challenges and considerations associated with deploying computational intelligence in surveillance, including privacy concerns, ethical considerations, and technical limitations - Explores specific use cases and applications where computational intelligence can enhance surveillance capabilities, such as object detection, activity recognition, anomaly detection, and predictive analytics

Table of contents

• Title of Book
• Chapter 1 Introduction to computational intelligence in surveillance
• Chapter 2 Fundamentals of image processing for surveillance
• Chapter 3 Integrated smart traffic ecosystem: A novel framework for real-time traffic management
• Chapter 4 Overview of computer vision techniques for object detection, tracking, and recognition
• Chapter 5 Use of AI in crowd movement and stampede detection: An explorative review
• Chapter 6 Development of non-gps drone mission control for 2 autonomous horizontal navigation
• Chapter 7 Dynamic visual understanding: Advancements in video segmentation with deep CNNs
• Chapter 8 Behavior analysis and anomaly detection
• Chapter 9 Applications of anomaly detection algorithms in identifying abnormal events
• Chapter 10 Public human assault prediction in surveillance video using artificial neural networks
• Chapter 11 Assessing wildfire severity and identifying hotspots using computational intelligence and image processing
• Chapter 12 Image acquisition and preprocessing: Methods for acquiring and preprocessing surveillance images
• Chapter 13 Integration of IoT sensors and AI platforms for advanced weather forecasting and environmental surveillance systems
• Chapter 14 Interoperability of computational intelligence with smart city infrastructure
• Chapter 15 Real-time vehicle detection embedded system for self-driving cars in challenging rainy conditions
• Chapter 16 The advancement of an automated guided vehicle for fire surveillance system
• Chapter 17 Enhancing IoT surveillance systems with edge computing: Optimizing adaptive bitrate streaming for improved Quality of Experience
• Chapter 18 Computational intelligence in surveillance systems using image processing integration with IoT and Smart Systems
• Chapter 19 Future trends and challenges in intelligent surveillance: Towards autonomous and predictive security systems
• Index