Activity recognition of interacting people
| Autor: | Nilay Tufek, Murat Yalcin, Hulya Yalcin |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Computer science business.industry Deep learning Pattern recognition 02 engineering and technology Skeleton (category theory) Convolutional neural network Data set Activity recognition 03 medical and health sciences 020901 industrial engineering & automation 0302 clinical medicine RGB color model Artificial intelligence Joint (audio engineering) business 030217 neurology & neurosurgery |
| Zdroj: | SIU |
| DOI: | 10.1109/siu.2018.8404173 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|