Journal Article

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

de López-Diz, Sergio, Losada-Gutiérrez, Cristina, Sánchez-Caballero, Adrián, Fuentes-Jimenez, David, Marrón-Romera, Marta, Casillas-Pérez, David, Sarker, Mohammad Ibrahim

2022

Overview

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 actions classification from the spatial and temporal encoded information of depth sequences. The use of depth data ensures that action recognition is carried out protecting people’s privacy, since their identities can not be recognized from these data. The proposed 3DFCNN has been optimized to reach a good performance in terms of accuracy while working in real-time. Then, it has been evaluated and compared with other state-of-the-art systems in three widely used public datasets with different characteristics, demonstrating that 3DFCNN outperforms all the non-DNN-based state-of-the-art methods with a maximum accuracy of 83.6% and obtains results that are comparable to the DNN-based approaches, while maintaining a much lower computational cost of 1.09 seconds, what significantly increases its applicability in real-world environments.

• Publisher: Springer US,Springer Nature B.V
• Subject: 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