Spatio-Temporal Human Activity Recognition using CNN and LSTM (Paperback)

Tarunima Chatterjee

ISBN 10: 6209136834 / ISBN 13: 9786209136832

Published by LAP Lambert Academic Publishing, 2025

About this Item

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

Bibliographic Details

• Title: Spatio-Temporal Human Activity Recognition using CNN and LSTM (Paperback)
• Publisher: LAP Lambert Academic Publishing
• Publication Date: 2025
• Language: English
• ISBN 10: 6209136834
• ISBN 13: 9786209136832
• Binding: Paperback
• Condition: new

About this title

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.