Citation

Bhuiyan, Md Roman and Abdullah, Junaidi and Hashim, Noramiza and Al Farid, Fahmid and Samsudin, Mohd Ali and Abdullah, Norra and Uddin, Jia (2021) Hajj pilgrimage video analytics using CNN. Bulletin of Electrical Engineering and Informatics, 10 (5). pp. 2598-2606. ISSN 2089-3191

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Abstract

This paper advances video analytics with a focus on crowd analysis for Hajj and Umrah pilgrimages. In recent years, there has been an increased interest in the advancement of video analytics and visible surveillance to improve the safety and security of pilgrims during their stay in Makkah. It is mainly because Hajj is an entirely special event that involve hundreds of thousands of people being clustered in a small area. This paper proposed a convolutional neural network (CNN) system for performing multitude analysis, in particular for crowd counting. In addition, it also proposes a new algorithm for applications in Hajj and Umrah. We create a new dataset based on the Hajj pilgrimage scenario in order to address this challenge. The proposed algorithm outperforms the state-of-the-art approach with a significant reduction of the mean absolute error (MAE) result: 240.0 (177.5 improvement) and the mean square error (MSE) result: 260.5 (280.1 improvement) when used with the latest dataset (HAJJ-Crowd dataset). We present density map and prediction of traditional approach in our novel HAJJ-crowd dataset for the purpose of evaluation with our proposed method.

Item Type:	Article
Uncontrolled Keywords:	Neural networks (Computer science), CNN, Crowd analysis, Crowd counting
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	04 Nov 2021 04:42
Last Modified:	04 Nov 2021 04:42
URII:	http://shdl.mmu.edu.my/id/eprint/9750

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