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Spatio-Temporal Human Activity Recognition using CNN and LSTM

By Tarunima Chatterjee

Spatio-Temporal Human Activity Recognition using CNN and LSTM

By Tarunima Chatterjee

0 - Default Title
Description
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.
Product details
Binding:
Paperback
Number of Pages:
68
Release Date:
2025-10-27
Publication Date:
2025-10-27
Publisher:
LAP LAMBERT Academic Publishing
Languages:
Original: English
ISBN10:
6209136834
ISBN13:
9786209136832
GPSR Manufacturer Reference:
[email protected]
Weight:
119 g
Height:
150 cm
Width:
220 cm
Thickness:
5 cm
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