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Synopsis
This book focuses on the design and development of an advanced Smart Video Surveillance System (SVSS) to address growing global security concerns. With the rapid increase in thefts and public safety issues, traditional surveillance systems are no longer sufficient, as they mainly perform basic tasks like face detection, object detection, and counting. The book highlights the need for deeper analysis of human activities in video data to detect suspicious behavior more effectively. The core contribution of the work is the proposal of a Human Activity Analysis Framework integrated with Fog Computing. This framework aims to process video data closer to the source (at the fog layer), enabling faster and more efficient real-time analysis. To achieve accurate human activity recognition, the authors introduce a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. This model captures both spatial (image-based) and temporal (sequence-based) features from video frames, significantly improving action recognition performance.
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Multi-View Human Activity Analysis Framework (Paperback)
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LAP Lambert Academic Publishing, 2026
ISBN 10: 620989481X
ISBN 13: 9786209894817
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Paperback. Condition: new. Paperback. This book focuses on the design and development of an advanced Smart Video Surveillance System (SVSS) to address growing global security concerns. With the rapid increase in thefts and public safety issues, traditional surveillance systems are no longer sufficient, as they mainly perform basic tasks like face detection, object detection, and counting. The book highlights the need for deeper analysis of human activities in video data to detect suspicious behavior more effectively. The core contribution of the work is the proposal of a Human Activity Analysis Framework integrated with Fog Computing. This framework aims to process video data closer to the source (at the fog layer), enabling faster and more efficient real-time analysis. To achieve accurate human activity recognition, the authors introduce a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. This model captures both spatial (image-based) and temporal (sequence-based) features from video frames, significantly improving action recognition performance. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9786209894817
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Multi-View Human Activity Analysis Framework
Published by
LAP Lambert Academic Publishing, 2026
ISBN 10: 620989481X
ISBN 13: 9786209894817
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MULTI-VIEW HUMAN ACTIVITY ANALYSIS FRAMEWORK
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LAP LAMBERT Academic Publishing Apr 2026, 2026
ISBN 10: 620989481X
ISBN 13: 9786209894817
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MULTI-VIEW HUMAN ACTIVITY ANALYSIS FRAMEWORK : FOR SMART VIDEO SURVEILLANCE
Published by
LAP LAMBERT Academic Publishing, 2026
ISBN 10: 620989481X
ISBN 13: 9786209894817
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book focuses on the design and development of an advanced Smart Video Surveillance System (SVSS) to address growing global security concerns. With the rapid increase in thefts and public safety issues, traditional surveillance systems are no longer sufficient, as they mainly perform basic tasks like face detection, object detection, and counting. The book highlights the need for deeper analysis of human activities in video data to detect suspicious behavior more effectively. The core contribution of the work is the proposal of a Human Activity Analysis Framework integrated with Fog Computing. This framework aims to process video data closer to the source (at the fog layer), enabling faster and more efficient real-time analysis. To achieve accurate human activity recognition, the authors introduce a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. This model captures both spatial (image-based) and temporal (sequence-based) features from video frames, significantly improving action recognition performance. Seller Inventory # 9786209894817
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MULTI-VIEW HUMAN ACTIVITY ANALYSIS FRAMEWORK | FOR SMART VIDEO SURVEILLANCE
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LAP LAMBERT Academic Publishing, 2026
ISBN 10: 620989481X
ISBN 13: 9786209894817
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Taschenbuch. Condition: Neu. MULTI-VIEW HUMAN ACTIVITY ANALYSIS FRAMEWORK | FOR SMART VIDEO SURVEILLANCE | Vikrant Sharma (u. a.) | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786209894817 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. Seller Inventory # 135390128
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MULTI-VIEW HUMAN ACTIVITY ANALYSIS FRAMEWORK
Published by
LAP LAMBERT Academic Publishing Apr 2026, 2026
ISBN 10: 620989481X
ISBN 13: 9786209894817
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 208 pp. Englisch. Seller Inventory # 9786209894817
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Multi-View Human Activity Analysis Framework (Paperback)
Published by
LAP Lambert Academic Publishing, 2026
ISBN 10: 620989481X
ISBN 13: 9786209894817
New
Paperback
Seller: CitiRetail, Stevenage, United Kingdom
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Paperback. Condition: new. Paperback. This book focuses on the design and development of an advanced Smart Video Surveillance System (SVSS) to address growing global security concerns. With the rapid increase in thefts and public safety issues, traditional surveillance systems are no longer sufficient, as they mainly perform basic tasks like face detection, object detection, and counting. The book highlights the need for deeper analysis of human activities in video data to detect suspicious behavior more effectively. The core contribution of the work is the proposal of a Human Activity Analysis Framework integrated with Fog Computing. This framework aims to process video data closer to the source (at the fog layer), enabling faster and more efficient real-time analysis. To achieve accurate human activity recognition, the authors introduce a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. This model captures both spatial (image-based) and temporal (sequence-based) features from video frames, significantly improving action recognition performance. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9786209894817
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