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.
1. Introduction - p. xxiii 2. Chapter 1 Introduction to computational intelligence in surveillance - p. 1 3. Chapter 2 Fundamentals of image processing for surveillance - p. 13 4. Chapter 3 Integrated smart traffic ecosystem: A novel framework for real-time traffic management - p. 25 5. Chapter 4 Overview of computer vision techniques for object detection, tracking, and recognition - p. 43 6. Chapter 5 Use of AI in crowd movement and stampede detection: An explorative review - p. 71 7. Chapter 6 Development of non-gps drone mission control for 2 autonomous horizontal navigation - p. 83 8. Chapter 7 Dynamic visual understanding: Advancements in video segmentation with deep CNNs - p. 97 9. Chapter 8 Behavior analysis and anomaly detection - p. 115 10. Chapter 9 Applications of anomaly detection algorithms in identifying abnormal events - p. 129 11. Chapter 10 Public human assault prediction in surveillance video using artificial neural networks - p. 145 12. Chapter 11 Assessing wildfire severity and identifying hotspots using computational intelligence and image processing - p. 161 13. Chapter 12 Image acquisition and preprocessing: Methods for acquiring and preprocessing surveillance images - p. 173 14. Chapter 13 Integration of IoT sensors and AI platforms for advanced weather forecasting and environmental surveillance systems - p. 189 15. Chapter 14 Interoperability of computational intelligence with smart city infrastructure - p. 209 16. Chapter 15 Real-time vehicle detection embedded system for self-driving cars in challenging rainy conditions - p. 227 17. Chapter 16 The advancement of an automated guided vehicle for fire surveillance system - p. 249 18. Chapter 17 Enhancing IoT surveillance systems with edge computing: Optimizing adaptive bitrate streaming for improved Quality of Experience - p. 263 19. Chapter 18 Computational intelligence in surveillance systems using image processing integration with IoT and Smart Systems - p. 279 20. Chapter 19 Future trends and challenges in intelligent surveillance: Towards autonomous and predictive security systems - p. 293 21. Index - p. 313
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.
1. Introduction - p. xxiii 2. Chapter 1 Introduction to computational intelligence in surveillance - p. 1 3. Chapter 2 Fundamentals of image processing for surveillance - p. 13 4. Chapter 3 Integrated smart traffic ecosystem: A novel framework for real-time traffic management - p. 25 5. Chapter 4 Overview of computer vision techniques for object detection, tracking, and recognition - p. 43 6. Chapter 5 Use of AI in crowd movement and stampede detection: An explorative review - p. 71 7. Chapter 6 Development of non-gps drone mission control for 2 autonomous horizontal navigation - p. 83 8. Chapter 7 Dynamic visual understanding: Advancements in video segmentation with deep CNNs - p. 97 9. Chapter 8 Behavior analysis and anomaly detection - p. 115 10. Chapter 9 Applications of anomaly detection algorithms in identifying abnormal events - p. 129 11. Chapter 10 Public human assault prediction in surveillance video using artificial neural networks - p. 145 12. Chapter 11 Assessing wildfire severity and identifying hotspots using computational intelligence and image processing - p. 161 13. Chapter 12 Image acquisition and preprocessing: Methods for acquiring and preprocessing surveillance images - p. 173 14. Chapter 13 Integration of IoT sensors and AI platforms for advanced weather forecasting and environmental surveillance systems - p. 189 15. Chapter 14 Interoperability of computational intelligence with smart city infrastructure - p. 209 16. Chapter 15 Real-time vehicle detection embedded system for self-driving cars in challenging rainy conditions - p. 227 17. Chapter 16 The advancement of an automated guided vehicle for fire surveillance system - p. 249 18. Chapter 17 Enhancing IoT surveillance systems with edge computing: Optimizing adaptive bitrate streaming for improved Quality of Experience - p. 263 19. Chapter 18 Computational intelligence in surveillance systems using image processing integration with IoT and Smart Systems - p. 279 20. Chapter 19 Future trends and challenges in intelligent surveillance: Towards autonomous and predictive security systems - p. 293 21. Index - p. 313
