Citation

Bhuiyan, Md Roman and Abdullah, Junaidi and Hashim, Noramiza and Al Farid, Fahmid and Uddin, Jia and Abdullah, Norra and Samsudin, Mohd Ali (2022) Crowd density estimation using deep learning for Hajj pilgrimage video analytics. F1000Research, 10. p. 1190. ISSN 2046-1402

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Abstract

This paper focuses on advances in crowd control study with an emphasis on high-density crowds, particularly Hajj crowds. Video analysis and visual surveillance have been of increasing importance in order to enhance the safety and security of pilgrimages in Makkah, Saudi Arabia. Hajj is considered to be a particularly distinctive event, with hundreds of thousands of people gathering in a small space, which does not allow a precise analysis of video footage using advanced video and computer vision algorithms. This research proposes an algorithm based on a Convolutional Neural Networks model specifically for Hajj applications. Additionally, the work introduces a system for counting and then estimating the crowd density. Methods: The model adopts an architecture which detects each person in the crowd, spots head location with a bounding box and does the counting in our own novel dataset (HAJJ-Crowd). Results: Our algorithm outperforms the state-of-the-art method, and attains a remarkable Mean Absolute Error result of 200 (average of 82.0 improvement) and Mean Square Error of 240 (average of 135.54 improvement). Conclusions: In our new HAJJ-Crowd dataset for evaluation and testing, we have a density map and prediction results of some standard methods.

Item Type:	Article
Uncontrolled Keywords:	Deep learning, Visual Surveillance, Density Estimation, Crowd Counting, CNN
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Mar 2022 03:07
Last Modified:	03 Mar 2022 03:07
URII:	http://shdl.mmu.edu.my/id/eprint/10027

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