Crowd Monitoring System Using Unmanned Aerial Vehicle （UAV）

Understanding and dealing with safety aspects of crowd dynamics in mass gatherings of people related to sports, religiousand cultural activities is very important, specifically with respect to crowd risk analysis and crowd safety. Historical trends from theKingdom of Saudi Arabia hosting millions of pilgrims each year during the Hajj and Omrah seasons suggest that stampedes in massgatherings occur frequently and highlight the importance of studying and dealing with the crowd dynamics more scientifically. In thisregard, efficient monitoring and other safe crowd management techniques have been used to minimize the risks associated with suchmass gathering. An example of these techniques is real-time monitoring of crowd using a UAV （Unmanned Aerial Vehicle）; thistechnique is becoming increasingly popular with the objective to save human lives, preserve environment, protect property, keep thepeace, and uphold governmental authority. In this paper, a crowd monitoring system for pedestrians has been proposed and tested. Thesystem has deployed crowd monitoring technique using real-time images taken by UAVs; the collected data was investigated, andcrowd 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/outlierswhich may lead to non-classification of features have been eliminated using image enhancement tools. The paper presents a crowdmonitoring system for pedestrians that can contribute to an area of research still in its infancy. The proposed system is a valuable tool interms of facilitating timely decisions, based on highly accurate information. The results show that the used image segmentationtechnique has the capability of mapping the crowd density with an accuracy level up to 80%.

Estimation of crowd density from UAVs images based on corner detection procedures and clustering analysis

With rapid developments in platforms and sensors technology in terms of digital cameras and video recordings,crowd monitoring has taken a considerable attentions in many disciplines such as psychology,sociology,engineering,and computer vision.This is due to the fact that,monitoring of the crowd is necessary to enhance safety and controllable movements to minimize the risk particularly in highly crowded incidents(e.g.sports).One of the platforms that have been extensively employed in crowd monitoring is unmanned aerial vehicles(UAVs),because UAVs have the capability to acquiring fast,low costs,high-resolution and real-time images over crowd areas.In addition,geo-referenced images can also be provided through integration of on-board positioning sensors(e.g.GPS/IMU)with vision sensors(digital cameras and laser scanner).In this paper,a new testing procedure based on feature from accelerated segment test(FAST)algorithms is introduced to detect the crowd features from UAV images taken from different camera orientations and positions.The proposed test started with converting a circle of 16 pixels surrounding the center pixel into a vector and sorting it in ascending/descending order.A single pixel which takes the ranking number 9(for FAST-9)or 12(for FAST-12)was then compared with the center pixel.Accuracy assessment in terms of completeness and correctness was used to assess the performance of the new testing procedure before and after filtering the crowd features.The results show that the proposed algorithms are able to extract crowd features from different UAV images.Overall,the values of Completeness range from 55 to 70%whereas the range of correctness values was 91 to 94%.

Spatiotemporal variability/stability analysis of NO2,CO,and land surface temperature(LST)during COVID-19 lockdown in Amman city,Jordan

The massive lockdown of human socioeconomic activities and vehicle movements due to the COVID-19 pandemic in 2020 has resulted in an unprecedented reduction in pollutant gases such as Nitrogen Dioxide(NO2)and Carbon Monoxide(CO)as well as Land Surface Temperature(LST)in Amman as well as all countries around the globe.In this study,the spatial and temporal variability/stability of NO2,CO,and LST throughout the lockdown period over Amman city have been analyzed.The NO2 and CO column density values were acquired from Sentinel-5p while the LST data were obtained from MODIS satellite during the lockdown period from 20 March to 24 April in 2019,2020,and 2021.The statistical analysis showed an overall reduction in NO2 in 2020 by around 27%and 48%compared to 2019 and 2021,respectively.However,an increase of 7%in 2021 compared to 2019 was observed because almost all anthropogenic activities were allowed during the daytime.The temporal persistence showed almost constant NO2 values in 2020 over the study area throughout the lockdown period.In addition,a slight decrease in CO(around 1%)was recorded in 2020 and 2021 compared to the same period in 2019.Restrictions on human activities resulted in an evident drop in LST in 2020 by around 13%and 18%less than the 5-year average and 2021 respectively.The study concludes that due to the restrictions imposed on industrial activities and automobile movements in Amman city,an unprecedented reduction in NO2,CO,and LST was recorded.