Abstract
In recent years, human activity recognition is becoming more popular in many areas such as human-robot interaction because of easy availability and widespread use of RGB-D sensors. The aim of this study is to automatically recognize human activities with deep learning techniques using three-dimensional skeletal joint data from the RGB-D sensor. Our methods uses the joint data directly and automatically acquires the features to be used in the classification, thus provides superiority to the methods which uses hand-crafted features. In our work, the NTU RGB + D dataset which is quite new and challenging compared to the datasets in the literature, is used. With using 2D, 3D Convolutional Neural Networks and LSTM Networks a performance analysis was performed. As a result of the experiments made, the technique applied by the 3D Convolutional Neural Network achieves the high classification accuracy with by obtaining much more meaningful features compare to the LSTM Network.
Original language | English |
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Title of host publication | 2018 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting, EBBT 2018 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1-5 |
Number of pages | 5 |
ISBN (Electronic) | 9781538651353 |
DOIs | |
Publication status | Published - 20 Jun 2018 |
Event | 4th Electric Electronics, Computer Science, Biomedical Engineerings' Meeting, EBBT 2018 - Istanbul, Turkey Duration: 18 Apr 2018 → 19 Apr 2018 |
Publication series
Name | 2018 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting, EBBT 2018 |
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Conference
Conference | 4th Electric Electronics, Computer Science, Biomedical Engineerings' Meeting, EBBT 2018 |
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Country/Territory | Turkey |
City | Istanbul |
Period | 18/04/18 → 19/04/18 |
Bibliographical note
Publisher Copyright:© 2018 IEEE.
Keywords
- 3D CNN
- Bidirectional LSTM
- Deep Learning
- LSTM
- interacting human activity recognition