Multi-Label Human Activity Recognition on Image Using Deep Learning

DOI

10.2991/itids-19.2019.26How to use a DOI?

Keywords

image recognition; computer vision; human activity recognition; machine learning; artificial neural networks; deep learning; convolutional neural networks; transfer learning

Abstract

This paper describes the model of convolutional neural network which is designed for multi-label human activity recognition. The possibilities of using activity recognition systems in the daily life of a person are considered. As part of this work, the study is conducted for the method of recognizing human activity on an image that can be obtained from a surveillance camera. To obtain more accurate recognition results, the network model used technology of transfer learning. Several pre-trained convolutional networks are considered using two types of transfer learning in order to find the best solution. The deep learning networks for solving the problem are implemented in Python using deep learning libraries. Considered models are trained to recognize binary multi-label human activity. Training and testing are performed on images collected by the author. The article also provides the obtained training and testing results of different models of convolutional neural networks. The data obtained are tabulated and also presented in graphical form.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Pavel Nikolaev
PY  - 2019/05
DA  - 2019/05
TI  - Multi-Label Human Activity Recognition on Image Using Deep Learning
BT  - Proceedings of the 7th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2019)
PB  - Atlantis Press
SP  - 141
EP  - 145
SN  - 1951-6851
UR  - https://doi.org/10.2991/itids-19.2019.26
DO  - 10.2991/itids-19.2019.26
ID  - Nikolaev2019/05
ER  -