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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Article
Crowd Monitoring System Using Unmanned Aerial Vehicle (UAV)
Author(s)
Ali Al-Sheary1 and Ali Almagbile2
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DOI:10.17265/1934-7359/2017.11.004
Affiliation(s)
1. Department of Civil Engineering, Umm Al-Qura University, Makkah 21955, Saudi Arabia;
2. Department of Geography, Yarmouk University, Irbid 21163, Jordan
ABSTRACT
Understanding and dealing with safety aspects of crowd dynamics in mass
gatherings of people related to sports, religious and cultural activities is
very important, specifically with respect to crowd risk analysis and crowd
safety. Historical trends from the Kingdom of Saudi Arabia hosting millions of
pilgrims each year during the Hajj and Omrah seasons suggest that
stampedes in mass gatherings occur frequently and highlight the importance of
studying and dealing with the crowd dynamics more scientifically. In this regard, efficient monitoring
and other safe crowd management techniques have been used to minimize the risks
associated with such mass gathering. An example of these techniques is
real-time monitoring of crowd using a UAV (Unmanned Aerial Vehicle); this
technique is becoming increasingly popular with the objective to save human
lives, preserve environment, protect property, keep the peace, and uphold
governmental authority. In this paper, a crowd monitoring
system for pedestrians has been proposed and
tested. The system has deployed crowd monitoring technique using real-time images taken by UAVs; the
collected data was investigated, and
crowd density was estimated using image segmentation procedures. A color-based
segmentation method has been employed to detect, identify and map crowd density
under different camera positions and orientations. Furthermore, the associated
anomalies/outliers which may lead to non-classification of features have been
eliminated using image enhancement tools. The paper presents a crowd monitoring
system for pedestrians that can contribute
to an area of research still in its infancy. The proposed system is a valuable
tool in terms of facilitating timely decisions, based on highly accurate
information. The results show that
the used image segmentation technique has the capability of mapping the crowd
density with an accuracy level up to 80%.
KEYWORDS
UAV, crowd monitoring, crowd density, geo-referencing, mapping.
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