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À propos de l'auteur
Uzair Bhatti (Senior Member, IEEE) was born in 1986. He received his Ph.D. degree in Information and Communication Engineering from Hainan University, Haikou, China. In 2021, he completed his postdoctoral research in Earth Observation and Remote Sensing at Nanjing, China. He is currently serving as an Associate Professor in the School of Information and Communication Engineering, Hainan University. Dr. Bhatti has published more than 300 research papers in internationally recognized journals and conferences. In addition, he is the inventor of 22 patents and has served as both Principal Investigator (PI) and participant in several national and provincial research-funded projects in China. He has also edited eight books and conference proceedings published by Springer Nature, Taylor & Francis, and IGI Global. His research interests include Artificial Intelligence, Intelligent Systems, Remote Sensing, Medical Image Analysis, Geographic Information Systems (GIS), and Machine Learning. He has served as a Session Chair at numerous IEEE conferences, including PRAI, DLCV, and IEEE FIT, and has also acted as an Editor and Guest Editor for several SCI- and EI-indexed journals
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Next-Generation Computer Vision Techniques for Video Analysis
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Hardcover. Etat : new. Hardcover. Cameras, connected devices, and sensor networks have multiplied fast, and the sheer volume of video data they generate has outgrown what traditional monitoring methods can handle. Simply detecting objects in a frame is not enough anymore. Recent advances in computer vision are pushing systems toward something closer to genuine understanding, recognizing activities, tracking events, and interpreting how people and objects interact within complex, changing environments. That shift is what is enabling a new generation of video analytics, ones that can maintain real situational awareness rather than just flag movement. The applications are already spreading fast, from security and transportation to healthcare, industry, and smart city environments, all relying on systems that can make sense of visual data in real time. Next-Generation Computer Vision Techniques for Video Analysis bridges the gap between theoretical deep learning architectures and their practical application in high-stakes environments, synthesizing fragmented research on temporal modeling to move beyond frame-by-frame analysis toward holistic video understanding. By connecting real-world challenges with a framework for ethical AI deployment, this book equips readers to build privacy-preserving video analytics systems that address bias mitigation in automated surveillance. Covering topics such as activity recognition, video segmentation, and vision language models, this book is an indispensable academic resource for graduate and doctoral students, computer vision researchers, security and surveillance system developers, smart city engineers, industrial automation engineers, policymakers, and more. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9798260009659
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Hardcover. Etat : new. Hardcover. Cameras, connected devices, and sensor networks have multiplied fast, and the sheer volume of video data they generate has outgrown what traditional monitoring methods can handle. Simply detecting objects in a frame is not enough anymore. Recent advances in computer vision are pushing systems toward something closer to genuine understanding, recognizing activities, tracking events, and interpreting how people and objects interact within complex, changing environments. That shift is what is enabling a new generation of video analytics, ones that can maintain real situational awareness rather than just flag movement. The applications are already spreading fast, from security and transportation to healthcare, industry, and smart city environments, all relying on systems that can make sense of visual data in real time. Next-Generation Computer Vision Techniques for Video Analysis bridges the gap between theoretical deep learning architectures and their practical application in high-stakes environments, synthesizing fragmented research on temporal modeling to move beyond frame-by-frame analysis toward holistic video understanding. By connecting real-world challenges with a framework for ethical AI deployment, this book equips readers to build privacy-preserving video analytics systems that address bias mitigation in automated surveillance. Covering topics such as activity recognition, video segmentation, and vision language models, this book is an indispensable academic resource for graduate and doctoral students, computer vision researchers, security and surveillance system developers, smart city engineers, industrial automation engineers, policymakers, and more. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798260009659
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Hardcover. Etat : new. Hardcover. Cameras, connected devices, and sensor networks have multiplied fast, and the sheer volume of video data they generate has outgrown what traditional monitoring methods can handle. Simply detecting objects in a frame is not enough anymore. Recent advances in computer vision are pushing systems toward something closer to genuine understanding, recognizing activities, tracking events, and interpreting how people and objects interact within complex, changing environments. That shift is what is enabling a new generation of video analytics, ones that can maintain real situational awareness rather than just flag movement. The applications are already spreading fast, from security and transportation to healthcare, industry, and smart city environments, all relying on systems that can make sense of visual data in real time. Next-Generation Computer Vision Techniques for Video Analysis bridges the gap between theoretical deep learning architectures and their practical application in high-stakes environments, synthesizing fragmented research on temporal modeling to move beyond frame-by-frame analysis toward holistic video understanding. By connecting real-world challenges with a framework for ethical AI deployment, this book equips readers to build privacy-preserving video analytics systems that address bias mitigation in automated surveillance. Covering topics such as activity recognition, video segmentation, and vision language models, this book is an indispensable academic resource for graduate and doctoral students, computer vision researchers, security and surveillance system developers, smart city engineers, industrial automation engineers, policymakers, and more. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. N° de réf. du vendeur 9798260009659
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Buch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Cameras, connected devices, and sensor networks have multiplied fast, and the sheer volume of video data they generate has outgrown what traditional monitoring methods can handle. Simply detecting objects in a frame is not enough anymore. Recent advances in computer vision are pushing systems toward something closer to genuine understanding, recognizing activities, tracking events, and interpreting how people and objects interact within complex, changing environments. That shift is what is enabling a new generation of video analytics, ones that can maintain real situational awareness rather than just flag movement. The applications are already spreading fast, from security and transportation to healthcare, industry, and smart city environments, all relying on systems that can make sense of visual data in real time. Next-Generation Computer Vision Techniques for Video Analysis bridges the gap between theoretical deep learning architectures and their practical application in high-stakes environments, synthesizing fragmented research on temporal modeling to move beyond frame-by-frame analysis toward holistic video understanding. By connecting real-world challenges with a framework for ethical AI deployment, this book equips readers to build privacy-preserving video analytics systems that address bias mitigation in automated surveillance. Covering topics such as activity recognition, video segmentation, and vision language models, this book is an indispensable academic resource for graduate and doctoral students, computer vision researchers, security and surveillance system developers, smart city engineers, industrial automation engineers, policymakers, and more. N° de réf. du vendeur 9798260009659
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