Citation

Aly, Cherry A. and Abas, Fazly Salleh and Goh, Hock Ann (2021) Robust video content analysis schemes for human action recognition. Science Progress, 104 (2). 003685042110054. ISSN 0036-8504

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Abstract

Action recognition is a challenging time series classification task that has received much attention in the recent past due to its importance in critical applications, such as surveillance, visual behavior study, topic discovery, security, and content retrieval. The main objective of the research is to develop a robust and high-performance human action recognition techniques. A combination of local and holistic feature extraction methods used through analyzing the most effective features to extract to reach the objective, followed by using simple and high-performance machine learning algorithms.

Item Type:	Article
Uncontrolled Keywords:	Human activity recognition
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	30 Jun 2021 16:26
Last Modified:	30 Jun 2021 16:26
URII:	http://shdl.mmu.edu.my/id/eprint/8787

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