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Publicado por LAP LAMBERT Academic Publishing, 2025
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Publicado por LAP LAMBERT Academic Publishing Okt 2025, 2025
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 68 pp. Englisch.
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Publicado por LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6209136834 ISBN 13: 9786209136832
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Añadir al carritoPaperback. Condición: new. Paperback. This book presents a robust Human Activity Recognition (HAR) system that integrates Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks, evaluated on the challenging UCF50 dataset. By combining CNNs' ability to extract spatial features from video frames with LSTMs' strength in modeling temporal sequences, the hybrid model accurately recognizes both simple and complex human actions unfolding over time. This approach addresses key HAR challenges, improving accuracy and generalization across diverse activities. Experimental results demonstrate enhanced precision and stability over conventional models. The system's versatility supports applications in surveillance, healthcare, sports analytics, and human-computer interaction. By bridging spatial and temporal learning, the book offers a scalable, real-world HAR solution adaptable to various environments, laying groundwork for future advances in activity recognition technologies. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6209136834 ISBN 13: 9786209136832
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book presents a robust Human Activity Recognition (HAR) system that integrates Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks, evaluated on the challenging UCF50 dataset. By combining CNNs' ability to extract spatial features from video frames with LSTMs' strength in modeling temporal sequences, the hybrid model accurately recognizes both simple and complex human actions unfolding over time. This approach addresses key HAR challenges, improving accuracy and generalization across diverse activities. Experimental results demonstrate enhanced precision and stability over conventional models. The system's versatility supports applications in surveillance, healthcare, sports analytics, and human-computer interaction. By bridging spatial and temporal learning, the book offers a scalable, real-world HAR solution adaptable to various environments, laying groundwork for future advances in activity recognition technologies.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 68 pp. Englisch.
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Publicado por LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6209136834 ISBN 13: 9786209136832
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering.
Idioma: Inglés
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Añadir al carritoTaschenbuch. Condición: Neu. Spatio-Temporal Human Activity Recognition using CNN and LSTM | Tarunima Chatterjee (u. a.) | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786209136832 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu Print on Demand.