| Yazarlar (5) |
Senem Tanberk
|
|
Zeynep Hilal Kilimci
|
|
Dilek Bilgin Tükel
|
|
Mitat Uysal
|
|
Selim Akyokuş
|
| Özet |
| Human activity recognition is a challenging problem with many applications including visual surveillance, human-computer interactions, autonomous driving and entertainment. In this study, we propose a hybrid deep model to understand and interpret videos focusing on human activity recognition. The proposed architecture is constructed combining dense optical flow approach and auxiliary movement information in video datasets using deep learning methodologies. To the best of our knowledge, this is the first study based on a novel combination of 3D-convolutional neural networks (3D-CNNs) fed by optical flow and long short-term memory networks (LSTM) fed by auxiliary information over video frames for the purpose of human activity recognition. The contributions of this paper are sixfold. First, a 3D-CNN, also called multiple frames is employed to determine the motion vectors. With the same purpose, the 3D ... |
| Anahtar Kelimeler |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | IEEE Access |
| Dergi ISSN | 1109-ACCE |
| Dergi Tarandığı Indeksler | |
| Dergi Grubu | Q1 |
| Makale Dili | İngilizce |
| Basım Tarihi | 01-2020 |
| Cilt No | 8 |
| Sayfalar | 19799 / 19809 |
