Multi view human activity analysis de sharma vikrant (7 resultados)

Condición: New.

Taschenbuch. Condición: 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.…

Paperback. Condición: 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.…

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 208 pp. Englisch.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 208 pp. Englisch.

Paperback. Condición: 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.…

Taschenbuch. Condición: 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. …