Deep learning approaches, platforms, datasets for behavior based recognition: a survey

Mohamme Jeddah, Yunusa and Hassan Abdalla Hashim, Aisha and Khalifa, Othman Omran (2025) Deep learning approaches, platforms, datasets for behavior based recognition: a survey. Indonesian Journal of Electrical Engineering and Computer Science, 38 (3). pp. 1880-1895. ISSN 2502-4752

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Abstract

Video surveillance is an extensively used tool due to the high rate of atypical behavior and many cameras that enable video capture and storage. Unfortunately, most of these cameras are operator dependent for stored content analysis. This limitation necessitates the provision of an automatic behavior identification system. This behavior identification can be achieved using unsupervised (generative) computer vision methods. Deep learning makes it possible to model human behavior regardless of where they could be. We attempt to classify current research work to report the ongoing trends in human behavior recognition using deep learning algorithms. This paper reviews various aspects, like the ones associated with machine learning and deep learning models, human activity recognition (HAR), deep learning frameworks/tools, abnormal behavior datasets, and a variety of other current trends in the field of automatic learning. All these are to give the researcher a sense of direction in this area.

Item Type:	Article (Journal)
Uncontrolled Keywords:	Behavior recognition; deep learning; Human activity recognition; Machine learning; Video surveillance
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Prof. Dr. Aisha Hassan Abdalla Hashim
Date Deposited:	16 May 2025 15:08
Last Modified:	16 May 2025 15:08
URI:	http://irep.iium.edu.my/id/eprint/121081

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