Free-viewpoint Motion Recognition Using Deep Alternative Learning

In this study, we aim to develop a robust motion recognition system for an intelligent video surveillance system, that can be used for security, sports and rehabilitation by using extended alternative learning. A robust motion recognition system is necessary for the automated detection of security i...

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Bibliographic Details
Published in:Denki Gakkai ronbunshi. D, Sangyō ōyō bumonshi 2021/02/01, Vol.141(2), pp.130-137
Main Authors: Nagayama, Itaru, Uehara, Wakaki, Shiroma, Yasushi, Miyazato, Takaya
Format: Article
Language:Japanese
Subjects:
3DCG
alternative learning
Artificial neural networks
Datasets
deep neural network
Machine learning
Motion perception
motion recognition
Neural networks
Recognition
Rehabilitation
Robustness
Security
security camera
Training
Video
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Summary:In this study, we aim to develop a robust motion recognition system for an intelligent video surveillance system, that can be used for security, sports and rehabilitation by using extended alternative learning. A robust motion recognition system is necessary for the automated detection of security incidents by using a machine learning approach. However, to avoid the difficulty of collecting a huge training dataset, we propose an alternative learning approach that trains a deep neural network with a 3D-CG dataset to recognize several motions. We present our experimental results on motion recognition from free-viewpoint videos by using deep learning and alternative learning. The trained deep neural network (DNN) is evaluated using actual videos by classifying the different actions performed by real humans in these videos.
ISSN:0913-6339
2187-1094
1348-8163
2187-1108
DOI:10.1541/ieejias.141.130