A Novel Action Recognition Framework Based on Deep-Learning and Genetic Algorithms

Creative Commons License

Yilmaz A. A., Güzel M. S., Bostancı G. E., Askerbeyli İ.

IEEE ACCESS, cilt.8, ss.100631-100644, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 8
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1109/access.2020.2997962
  • Dergi Adı: IEEE ACCESS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Sayfa Sayıları: ss.100631-100644
  • Anahtar Kelimeler: Computer architecture, Training, Feature extraction, Optimization, Convolution, Computer vision, Computational modeling, Human action recognition, DNNs, transfer learning, deep learning, optimization, genetic algorithm, FEATURE-SELECTION, SENSORS
  • Ankara Üniversitesi Adresli: Evet

Özet

Recognition of human actions in partially cluttered environments is an important research field of computer vision and human-computer interaction. This field has recently garnered attention from a large number of academic researchers in various fields of application. This study proposes a novel deep-learning-based architecture for the recognition and prediction of human actions based on a hybrid model. The main contribution of this study is to propose a new hybrid architecture, integrating four wide-ranging pre-trained network models in an optimized manner, using a metaheuristic algorithm. This architecture consists of four main stages: namely, the creation of the data set, the design of deep neural network (DNN) architecture, training and optimization of the proposed DNN architecture, and evaluation of the trained DNN. By adapting the aforementioned architecture, reliable features are obtained for the training procedure. In order to validate the superiority of the proposed architecture over other state-of-the-art studies, a performance evaluation between these architectures is presented using benchmark datasets. The results reveal that the proposed architecture outperforms previously developed architectures in terms of predicting human actions.