Optimised ML-based System Model for Adult-Child Actions Recognition

Many critical applications require accurate real-time human action recognition. However, there are many hurdles associated with capturing and pre-processing image data, calculating features, and classification because they consume significant resources for both storage and computation. To circumvent...

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Bibliographic Details
Published in:KSII transactions on Internet and information systems 2019, 13(2), , pp.929-944
Main Authors: Alhammami, Muhammad, Hammami, Samir Marwan, Ooi, Chee-Pun, Tan, Wooi-Haw
Format: Article
Language:English
Subjects:
Activities of daily living
Analysis
Machine learning
Methods
Object recognition (Computers)
Pattern recognition
컴퓨터학
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Summary:Many critical applications require accurate real-time human action recognition. However, there are many hurdles associated with capturing and pre-processing image data, calculating features, and classification because they consume significant resources for both storage and computation. To circumvent these hurdles, this paper presents a recognition machine learning (ML) based system model which uses reduced data structure features by projecting real 3D skeleton modality on virtual 2D space. The MMU VAAC dataset is used to test the proposed ML model. The results show a high accuracy rate of 97.88% which is only slightly lower than the accuracy when using the original 3D modality-based features but with a 75% reduction ratio from using RGB modality. These results motivate implementing the proposed recognition model on an embedded system platform in the future. Keywords: Human action recognition, 2D Skeleton features, 3D Projection, Reduced data structure, Compound features selection method
ISSN:1976-7277
1976-7277
DOI:10.3837/tiis.2019.02.024