Classification of human actions using 3D skeleton data: A performance comparison between classical machine learning and deep learning models
Year: 2024
Type: article
Abstract: This study investigates the effectiveness of 3D skeleton data for human action recognition by comparing the classification performance of machine learning and deep learning models.We use the subset of the NTU RGB+D dataset, containing only frontal-view recordings of 40 individuals performing 60 different actions.Our study uses linear discriminant analysis (LDA), support vector machine (SVM), and random forest (RF) as machine learning models, while the deep learning models are hierarchical bidirectional RNN (HBRNN) and semantics-guided neural network (SGN).To evaluate model performance, cross-subject cross-validation is conducted.Our analysis demonstrates that action type significantly impacts model performance.Cluster analysis by action category shows no significant difference in classification performance between machine learning and deep learning models for easily recognizable actions.However, for actions requiring precise differentiation based on frontal-view joint coordinates such as 'clapping' or 'rubbing hands', deep learning models show a higher performance in capturing subtle joint movements compared to machine learning models.
Authors: Juhwan Kim, Jongchan Kim, Sungim Lee
Institution: Dankook University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Award: G1674953209
OpenAlex ID: W4403801934