3DFCNN: real-time action recognition using 3D deep neural networks with raw depth information

This work describes an end-to-end approach for real-time human action recognition from raw depth image-sequences. The proposal is based on a 3D fully convolutional neural network, named 3DFCNN, which automatically encodes spatio-temporal patterns from raw depth sequences. The described 3D-CNN allows...

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Veröffentlicht in:	Multimedia tools and applications 2022-07, Vol.81 (17), p.24119-24143
Hauptverfasser:	Sánchez-Caballero, Adrián, de López-Diz, Sergio, Fuentes-Jimenez, David, Losada-Gutiérrez, Cristina, Marrón-Romera, Marta, Casillas-Pérez, David, Sarker, Mohammad Ibrahim
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Computer Communication Networks Computer Science Data Structures and Information Theory Human activity recognition Human motion Multimedia Information Systems Neural networks Object recognition Real time Special Purpose and Application-Based Systems
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