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Abstract
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Recognizing various human actions in videos is considered a highly complicated problem, which has many potential applications in solving real-world problems such as human behavior analysis, artificial intelligence, video surveillance, and smart manufacturing. Therefore, designing novel approaches for automatically understanding video data is highly demanded. Towards this goal, different algorithms have been investigated, concentrating on extracting the spatial information and the temporal dependencies. However, motion feature extraction is engineered and isolated from the learning operations. In this paper, to comprehend motion features along with the spatial information and the time dependencies, an innovative attempt is made by designing a new Gated Recurrent Unit (GRU) network. Moreover, a novel deep neural network is presented using the proposed GRU to recognize human actions. Evaluations on popular datasets (UCF50, UCF101, and HMDB51) not only convey the superiority of the proposed GRU in action recognition using an end-to-end learning model but also emphasize on the generalizability of the proposed method. Additionally, to show the applicability and functionality of the proposed model in solving real-world problems, an engine block assembly dataset was collected and the performance of the proposed method was measured on this dataset. Finally, the robustness of the proposed method against various kinds of noise was tested. The obtained results demonstrate the high performance of the proposed method and its robustness against noise.
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