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Action Recognition for Multiview Skeleton 3D Data Using NTURGB + D Dataset

Rosepreet Kaur Bhogal1,*, V. Devendran2

1 School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, 144411, India
2 School of Computer Science Engineering, Lovely Professional University, Phagwara, 144411, India

* Corresponding Authors: Rosepreet Kaur Bhogal. Email: email,email

(This article belongs to the Special Issue: Intrusion Detection and Trust Provisioning in Edge-of-Things Environment)

Computer Systems Science and Engineering 2023, 47(3), 2759-2772. https://doi.org/10.32604/csse.2023.034862

Abstract

Human activity recognition is a recent area of research for researchers. Activity recognition has many applications in smart homes to observe and track toddlers or oldsters for their safety, monitor indoor and outdoor activities, develop Tele immersion systems, or detect abnormal activity recognition. Three dimensions (3D) skeleton data is robust and somehow view-invariant. Due to this, it is one of the popular choices for human action recognition. This paper proposed using a transversal tree from 3D skeleton data to represent videos in a sequence. Further proposed two neural networks: convolutional neural network recurrent neural network_1 (CNN_RNN_1), used to find the optimal features and convolutional neural network recurrent neural network network_2 (CNN_RNN_2), used to classify actions. The deep neural network-based model proposed CNN_RNN_1 and CNN_RNN_2 that uses a convolutional neural network (CNN), Long short-term memory (LSTM) and Bidirectional Long short-term memory (BiLSTM) layered. The system efficiently achieves the desired accuracy over state-of-the-art models, i.e., 88.89%. The performance of the proposed model compared with the existing state-of-the-art models. The NTURGB + D dataset uses for analyzing experimental results. It is one of the large benchmark datasets for human activity recognition. Moreover, the comparison results show that the proposed model outperformed the state-of-the-art models.

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APA Style
Bhogal, R.K., Devendran, V. (2023). Action recognition for multiview skeleton 3D data using NTURGB + D dataset. Computer Systems Science and Engineering, 47(3), 2759-2772. https://doi.org/10.32604/csse.2023.034862
Vancouver Style
Bhogal RK, Devendran V. Action recognition for multiview skeleton 3D data using NTURGB + D dataset. Comput Syst Sci Eng. 2023;47(3):2759-2772 https://doi.org/10.32604/csse.2023.034862
IEEE Style
R.K. Bhogal and V. Devendran, "Action Recognition for Multiview Skeleton 3D Data Using NTURGB + D Dataset," Comput. Syst. Sci. Eng., vol. 47, no. 3, pp. 2759-2772. 2023. https://doi.org/10.32604/csse.2023.034862



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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