Open Access

ARTICLE

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

Rosepreet Kaur Bhogal^1,*, 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: ,

(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

Received 29 July 2022; Accepted 21 December 2022; Issue published 09 November 2023

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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