• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

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


Abstract

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.


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Cite this article

[IEEE Style]
M. Alhammami, S. M. Hammami, C. Ooi, W. Tan, "Optimised ML-based System Model for Adult-Child Actions Recognition," KSII Transactions on Internet and Information Systems, vol. 13, no. 2, pp. 929-944, 2019. DOI: 10.3837/tiis.2019.02.024.

[ACM Style]
Muhammad Alhammami, Samir Marwan Hammami, Chee-Pun Ooi, and Wooi-Haw Tan. 2019. Optimised ML-based System Model for Adult-Child Actions Recognition. KSII Transactions on Internet and Information Systems, 13, 2, (2019), 929-944. DOI: 10.3837/tiis.2019.02.024.

[BibTeX Style]
@article{tiis:22014, title="Optimised ML-based System Model for Adult-Child Actions Recognition", author="Muhammad Alhammami and Samir Marwan Hammami and Chee-Pun Ooi and Wooi-Haw Tan and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2019.02.024}, volume={13}, number={2}, year="2019", month={February}, pages={929-944}}