Citation

Bhuiyan, Md Roman and Abdullah, Junaidi and Hashim, Noramiza and Al Farid, Fahmid and Uddin, Jia (2023) Hajj pilgrimage abnormal crowd movement monitoring using optical flow and FCNN. Journal of Big Data, 10 (1). ISSN 2196-1115

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Abstract

This article discusses an effective technique for detecting abnormalities in Hajj crowd videos. In order to guarantee the identification of anomalies in scenes, a trained and supervised FCNN is turned into an FCNN using FCNNs and temporal data. By minimizing computational complexity, incorrect movement detection is utilized to achieve high performance in terms of speed and precision. This FCNN-based architecture is designed to handle two primary tasks: feature representation and the detection of incorrect movement outliers. Additionally, to overcome the aforementioned issues, this research will generate a new crowd anomaly video dataset based on the Hajj pilgrimage scenario. On the proposed dataset, the UCSD Ped2, Subway Entry, and Subway Exit datasets, the proposed FCNN-based technique obtained ultimate accuracy of 100%, 90%, 95%, and 89%, respectively. Additionally, the ResNet50-based technique achieved ultimate accuracy of 96%, 89%, 94%, and 92%, respectively, for the proposed dataset, the UCSD Ped2, Subway Entry, and Subway Exit datasets.

Item Type:	Article
Uncontrolled Keywords:	Crowd anomaly classification, FCNN, Optical Flow and Hajj crowd video dataset
Subjects:	Q Science > QC Physics > QC350-467 Optics. Light
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Jul 2023 03:07
Last Modified:	03 Jul 2023 03:07
URII:	http://shdl.mmu.edu.my/id/eprint/11475

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