A new hybrid deep learning model for human action recognition

Human behavior has been always an important factor in social communication. The human activity and action recognition are all clues that facilitate the analysis of human behavior. Human action recognition is an important challenge in a variety of application including human-computer interaction and intelligent video surveillance to enhance security in different domains. The evaluation algorithm relies on the proper extraction and the learning data. The success of the deep learning led to many imposing results in several contexts that include neural network. Here the emergence of Gated Recurrent Neural Networks with increased computation powers is being adopted for sequential data and video classification. However, to have an efficient classifier for assigning the class label, it is very necessary to have a strong features vector. Features are the most important information in each data. Indeed, features extraction can influence on the performance of the algorithm and the computation complexity. This paper proposes a novel approach for human action recognition based on hybrid deep learning model. The proposed approach is evaluated on the challenging UCF Sports, UCF101 and KTH datasets. An average of 96.3% is obtained when we have tested on KTH dataset.