A hybrid deep model using deep learning and dense optical flow approaches for human activity recognition
Yazarlar (5)
Senem Tanberk
Zeynep Hilal Kilimci
Dilek Bilgin Tükel
Mitat Uysal
Selim Akyokuş
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | IEEE Access (Q1) | ||
| Dergi Tarandığı Indeksler | |||
| Makale Dili | İngilizce | Basım Tarihi | 01-2020 |
| Cilt / Sayı / Sayfa | 8 / 0 / 19799–19809 | DOI | – |
| Makale Linki | https://ieeexplore.ieee.org/abstract/document/8964371/ | ||
| UAK Araştırma Alanları |
Yapay Zeka
Görüntü İşleme
Makine Öğrenmesi
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| 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 |
