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| Title | Artificial Intelligence In Indoor Or Outdoor Surveillance Systems: A Systematic View, Principles, Challenges And Applications |
|---|---|
| ID_Doc | 10497 |
| Authors | Gupta V.; Bansal T.; Yadav V.K.; Bhowmik D. |
| Year | 2024 |
| Published | Mathematical Models Using Artificial Intelligence for Surveillance Systems |
| DOI | http://dx.doi.org/10.1002/9781394200733.ch13 |
| Abstract | In recent years, the integration of Artificial Intelligence (AI) into indoor and outdoor surveillance systems has revolutionized the field of security and monitoring. This book chapter provides a comprehensive and systematic exploration of the role of AI in surveillance, offering a detailed overview of its principles, challenges, and wide-ranging applications. The chapter begins with an examination of the fundamental principles underlying AI-driven surveillance systems. It delves into the core concepts of machine learning, computer vision, and deep learning, elucidating how these technologies are harnessed to enhance the intelligence of surveillance systems. Special attention is given to the advancements in object recognition, behavior analysis, and anomaly detection, which are pivotal components of modern surveillance AI. Addressing the challenges associated with indoor and outdoor surveillance, the chapter discusses issues such as data privacy, scalability, and real-time processing. It also explores the integration of sensor networks, drones, and edge computing to overcome these challenges, enabling the development of highly responsive and efficient surveillance systems. The practical applications of AI in surveillance are explored in depth. From smart cities and critical infrastructure protection to retail analytics and wildlife conservation, AI-powered surveillance systems find utility in diverse domains. Case studies and real-world examples illustrate the tangible impact of AI in improving security, safety, and operational efficiency. Furthermore, the chapter highlights the ethical considerations and regulatory frameworks that govern the deployment of AI in surveillance. It discusses the importance of transparency, accountability, and bias mitigation to ensure that AI-powered surveillance systems are used responsibly and in accordance with societal norms. The chapter concludes by discussing the research gaps in the field of AI for surveillance systems. These gaps include the need for more research on the use of AI for crowd analysis, behaviour analysis, and anomaly detection. There is also a need for more research on the ethical implications of using AI in surveillance systems. © 2024 Scrivener Publishing LLC. All rights reserved. |
| Author Keywords | Anomaly detection; Artificial intelligence; Crowd analysis; Face recognition; Object detection; Surveillance systems |
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