MoveNET Enabled Neural Network for Fast Detection of Physical Bullying in Educational Institutions

Abstract: In this article, we provide a MoveNET-based technique that we think may be used to detect violent actions. This strategy does not need high-computational technology, and it is able to put into action in a very short amount of time. Our method is comprised of two stages: first, the capture of features from photo sequences in order to evaluate body position; next, the application of an artificial neural network to activities classification in order to determine whether or not the picture frames include violent or hostile circumstances. A video aggression database consisting of 400 minutes of one individual's actions and 20 hours of videodata encompassing physical abuse, as well as 13 categories for distinguishing between the behaviors of the attacker and the victim, was created. In the end, the suggested approach was refined and validated by employing the collected dataset during the process. According to the findings, an accuracy rate of 98% was attained while attempting to detect aggressive behavior in video sequences. In addition, the findings indicate that the suggested technique is able to identify aggressive behavior and violent acts in a very short amount of time and is suitable for use in apps that take place in the real world.

Zhadra Kozhamkulova, Bibinur Kirgizbayeva, Gulbakyt Sembina, Ulmeken Smailova, Madina Suleimenova, Arailym Keneskanova and Zhumakul Baizakova. “MoveNET Enabled Neural Network for Fast Detection of Physical Bullying in Educational Institutions”. International Journal of Advanced Computer Science and Applications (IJACSA) 14.5 (2023). http://dx.doi.org/10.14569/IJACSA.2023.0140578

@article{Kozhamkulova2023,
title = {MoveNET Enabled Neural Network for Fast Detection of Physical Bullying in Educational Institutions},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140578},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140578},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {5},
author = {Zhadra Kozhamkulova and Bibinur Kirgizbayeva and Gulbakyt Sembina and Ulmeken Smailova and Madina Suleimenova and Arailym Keneskanova and Zhumakul Baizakova}
}