3DFCNN: Real-Time Action Recognition using 3D Deep Neural Networks with Raw Depth Information

• URL: http://arxiv.org/abs/2006.07743v1
• Date: Sat, 13 Jun 2020 23:24:07 GMT
• Title: 3DFCNN: Real-Time Action Recognition using 3D Deep Neural Networks with Raw Depth Information
• Authors: Adrian Sanchez-Caballero, Sergio de L\'opez-Diz, David Fuentes-Jimenez, Cristina Losada-Guti\'errez, Marta Marr\'on-Romera, David Casillas-Perez, Mohammad Ibrahim Sarker
• Abstract summary: This paper describes an approach for real-time human action recognition from raw depth image-sequences, provided by an RGB-D camera. The proposal is based on a 3D fully convolutional neural network, named 3DFCNN, which automatically encodes-temporal patterns from depth sequences without %any costly pre-processing.
• Score: 1.3854111346209868
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human actions recognition is a fundamental task in artificial vision, that has earned a great importance in recent years due to its multiple applications in different areas. %, such as the study of human behavior, security or video surveillance. In this context, this paper describes an approach for real-time human action recognition from raw depth image-sequences, provided by an RGB-D camera. The proposal is based on a 3D fully convolutional neural network, named 3DFCNN, which automatically encodes spatio-temporal patterns from depth sequences without %any costly pre-processing. Furthermore, the described 3D-CNN allows %automatic features extraction and 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% allows recognizing the actions carried out by people, protecting their privacy%\sout{of them} , since their identities can not be recognized from these data. %\st{ from depth images.} 3DFCNN has been evaluated and its results compared to those from other state-of-the-art methods within three widely used %large-scale NTU RGB+D datasets, with different characteristics (resolution, sensor type, number of views, camera location, etc.). The obtained results allows validating the proposal, concluding that it outperforms several state-of-the-art approaches based on classical computer vision techniques. Furthermore, it achieves action recognition accuracy comparable to deep learning based state-of-the-art methods with a lower computational cost, which allows its use in real-time applications.

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