Citation

Bhuiyan, Md Roman and Abdullah, Junaidi and Hashim, Noramiza and Al Farid, Fahmid (2022) Video analytics using deep learning for crowd analysis: a review. Multimedia Tools and Applications, 81 (19). pp. 27895-27922. ISSN 1380-7501; eISSN: 1573-7721

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Abstract

Gathering a large number of people in a shared physical area is very common in urban culture. Although there are limitless examples of mega crowds, the Islamic religious ritual, the Hajj, is considered as one of the greatest crowd scenarios in the world. The Hajj is carried out once in a year with a congregation of millions of people when the Muslims visit the holy city of Makkah at a given time and date. Such a big crowd is always prone to public safety issues, and therefore requires proper measures to ensure safe and comfortable arrangement. Through the advances in computer vision based scene understanding, automatic analysis of crowd scenes is gaining popularity. However, existing crowd analysis algorithms might not be able to correctly interpret the video content in the context of the Hajj. This is because the Hajj is a unique congregation of millions of people crowded in a small area, which can overwhelm the use of existing video and computer vision based sophisticated algorithms. Through our studies on crowd analysis, crowd counting, density estimation, and the Hajj crowd behavior, we faced the need of a review work to get a research direction for abnormal behavior analysis of Hajj pilgrims. Therefore, this review aims to summarize the research works relevant to the broader field of video analytics using deep learning with a special focus on the visual surveillance in the Hajj. The review identifies the challenges and leading-edge techniques of visual surveillance in general, which may gracefully be adaptable to the applications of Hajj and Umrah. The paper presents detailed reviews on existing techniques and approaches employed for crowd analysis from crowd videos, specifically the techniques that use deep learning in detecting abnormal behavior. These observations give us the impetus to undertake a painstaking yet exhilarating journey on crowd analysis, classification and detection of any abnormal movement of the Hajj pilgrims. Furthermore, because the Hajj pilgrimage is the most crowded domain for video-related extensive research activities, this study motivates us to critically analyze the crowd on a large scale.

Item Type:	Article
Uncontrolled Keywords:	Deep learning, Crowd analysis, Abnormal behavior, Video surveillances
Subjects:	Q Science > QA Mathematics > QA801-939 Analytic mechanics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Nov 2022 01:56
Last Modified:	03 Nov 2022 02:01
URII:	http://shdl.mmu.edu.my/id/eprint/10195

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