Synopsis
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.
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Spatio-Temporal Human Activity Recognition using CNN and LSTM
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ISBN 13: 9786209136832
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Spatio-Temporal Human Activity Recognition using CNN and LSTM
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LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6209136834
ISBN 13: 9786209136832
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Spatio-Temporal Human Activity Recognition using CNN and LSTM
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LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6209136834
ISBN 13: 9786209136832
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Spatio-Temporal Human Activity Recognition using CNN and LSTM (Paperback)
Published by
LAP Lambert Academic Publishing, 2025
ISBN 10: 6209136834
ISBN 13: 9786209136832
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Paperback. Condition: 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 multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9786209136832
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Spatio-Temporal Human Activity Recognition using CNN and LSTM
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Spatio-Temporal Human Activity Recognition using CNN and LSTM (Paperback)
Published by
LAP Lambert Academic Publishing, 2025
ISBN 10: 6209136834
ISBN 13: 9786209136832
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Paperback. Condition: 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 Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9786209136832
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Spatio-Temporal Human Activity Recognition using CNN and LSTM
Published by
LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6209136834
ISBN 13: 9786209136832
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Spatio-Temporal Human Activity Recognition using CNN and LSTM
Published by
LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6209136834
ISBN 13: 9786209136832
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Spatio-Temporal Human Activity Recognition using CNN and LSTM (Paperback)
Published by
LAP Lambert Academic Publishing, 2025
ISBN 10: 6209136834
ISBN 13: 9786209136832
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Paperback. Condition: 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. Seller Inventory # 9786209136832
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Spatio-Temporal Human Activity Recognition using CNN and LSTM
Published by
LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6209136834
ISBN 13: 9786209136832
New
Softcover
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