An average of 96.3% is obtained when we have tested on KTH dataset. The proposed approach is evaluated on the challenging UCF Sports, UCF101 and KTH datasets. This paper proposes a novel approach for human action recognition based on hybrid deep learning model. Indeed, features extraction can influence on the performance of the algorithm and the computation complexity. Features are the most important information in each data. However, to have an efficient classifier for assigning the class label, it is very necessary to have a strong features vector. Here the emergence of Gated Recurrent Neural Networks with increased computation powers is being adopted for sequential data and video classification. ![]() ![]() The success of the deep learning led to many imposing results in several contexts that include neural network. The evaluation algorithm relies on the proper extraction and the learning data. 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. In the suggested method, first, the hand gesture is extracted from. In this paper a novel and real-time approach for hand gesture recognition system is presented. Hand gesture recognition possesses extensive applications in virtual reality, sign language recognition, and computer games. The human activity and action recognition are all clues that facilitate the analysis of human behavior. Matlab Code for Simple Gesture Recognition. ![]() Experimental results demonstrated that the proposed. Human behavior has been always an important factor in social communication. The performance evaluation considers five human actions (digging, waving, throwing, walking, and running).
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