Crowd Monitoring (original) (raw)
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CrowdTracing: Overcrowding Clustering and Detection System for Social Distancing
Maintaining social distancing in public spaces plays a pivotal role in decreasing COVID-19 contagion and viral spread. COVID-19 has required many countries around the world to close work places, schools and public spaces. This has prompted policy makers, venue managers and local authorities to investigate practical mitigation strategies using technology to exit the lockdown safely and enable the reopening of cities and public spaces. This paper introduces CrowdTracing, a dynamic overcrowding detection system that encourages social-distancing and triggers an alert to venue, city council or facility managers in a dynamic and privacy-preserving manner. CrowdTracing utilises ubiquitous WiFi probing and density-based clustering techniques which can be performed in real-time to identify commonly crowded areas and assist in the estimation of excess gatherings. The proposed system can also be used to enable discovery of where social distancing rules are not being followed, enabling a rapid response, controlling or slowing down the spread of the virus. A classification recall of 0.85 on an experiment with 1000 simulated scenarios were achieved. This indicates the CrowdTracing system proposed was able to identify 85 out 100 scenarios in which social distancing rules were not being followed.
Capturing crowd dynamics at large scale events using participatory GPS-localization
2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2014
Large-scale festivals with a multitude of stages, food stands, and attractions require a complex perimeter design and program planning in order to manage the mobility of crowds as a controlled process. Errors in the planning phase can cause unexpected crowd dynamics and lead to stampedes with lethal consequences. We deployed an official app for Züri Fäscht 2013 -the largest Swiss event -over a period of three days. The app offered information about the festival and featured a background localization allowing us to collect continuously the visitor position. With 56.000 app downloads and 28.000 users contributing 25M location updates in total, we obtained a large scale dataset. By aggregation of location points complex crowd dynamics can be captured during the entire festival. In this paper we present the data collection for Züri Fäscht 2013 and best practises to acquire as many contributing users as possible for such an event. Furthermore, we show the potential of aggregated location data and visualize relevant parameters that can serve as tool for analysis and planning of program and perimeter design.
Managing Crowds with Wireless and Mobile Technologies
Wireless Communications and Mobile Computing, 2018
Thousands of people have lost their lives in stampedes and other crowd related disasters in recent years. Most of these fatalities seem to have been caused by poor control and management of crowds, which is discussed in this article. An efficient and effective crowd management system must also have a plan to deal with the ongoing threat of terrorism and outbreak of various kinds of communicable diseases. In this article, we present a framework of a Crowd Control and Health Management System specially designed to prevent and manage stampedes and other disasters. The system has two subsystems; one for dealing with the management of stampedes and other disasters and the other with healthcare management. As part of the proposed system, an algorithm for an early detection of stampedes, with proof and simulation of implementation, is provided. As part of the healthcare management subsystem, we integrate several mobile applications and develop four of them dealing with relief issues, blood...
In-Depth Survey to Detect, Monitor and Manage Crowd
IEEE Access
Crowd management is a flourishing, active research area and must be given attention due to the potential losses, disasters, and accidents that could occur if it were neglected. For the last decade, the crowd management field has witnessed significant advancements; however, more investigative work is still needed. The integration of different crowd detection and monitoring techniques can enhance the control and the performance compared to those of more limited stand-alone techniques. Crowd management encompasses an entire process, from the monitoring stage through the decision support system stage. This sector involves accessing and interpreting information sources, predicting crowd behavior, and deciding on the use of a range of possible interventions based on context. This paper shows a fresh conclusive review of the concept of the crowd, discussing it from several perspectives in light of its defining characteristics, its risks, and tragedies, which may occur due to challenges faced during crowd management, where these conclusions are based on a massive number of scholarly articles that were newly published. Besides, a systematic discussion is shown concerning the steps of managing a crowd, including crowd detection, in which several new methods are reviewed, followed by illustrating both direct and indirect approaches to crowd monitoring and tracking monitoring. The primary purpose of this review is to establish a comprehensive understanding of crowdrelated processes. Moreover, it aims to find research gaps to overcome the limitations of using stand-alone techniques in each process and provide support to other researchers' future work.
Big Data Framework for Crowd Monitoring in Large Crowded Events
UMT Artificial Intelligence Review, 2023
The management of large events with hundreds of thousands of individuals has remained a challenge over the years. Crushes and stampedes occurring in the events of mass gathering have swallowed many valuable lives around the world. Considering the substantial advancement in positional tracking, wearable technology, and wireless communication, many event organizers are embracing the use of these technologies to get assistance in managing large events. Intelligent monitoring of crowd movement and timely analysis of evolving conditions may aid in early detection of critical situations. The current research aims to propose a big data resource framework to model, simulate, and visualize the crowd conditions for actual venue settings. A distributed framework has been presented to monitor the movement and interaction of individuals in large crowded events through localized sensing and geospatial analysis of massive positional data. The pilgrimage (Hajj) has been considered as a case study for demonstrating the effectiveness of the proposed framework. The proposed framework has been with the help of synthetic data that covered some useful and frequent scenarios based on the case study of pilgrimage (hajj), which is an annual event involving more than a million people.
Crowd Management Challenges: Tackling Approach for Real Time Crowd Monitoring
2019
Due to the growing population, crowd management became an important issue and a challenge for the security agencies across the world, where effective crowd management can prevent serious accidents, and in some cases, mortalities. Thus, a great number of researchers haven’t saved any effort in their trials to find a proper solution for crowd management. However, monitoring a large area of crowd is a task full of obstacles and challenges. Crowd management per se is not a simple procedure; it includes some challenging and consecutive steps which are proper crowd analysis, identification, and monitoring and anomalous activity detection. This paper attempts to sum up most of the available and presented works that are concerned about crowd management and monitoring with presenting a tackling approach that will be more accurate for real-time crowd monitoring.
Probing crowd density through smartphones in city-scale mass gatherings
EPJ Data Science, 2013
City-scale mass gatherings attract hundreds of thousands of pedestrians. These pedestrians need to be monitored constantly to detect critical crowd situations at an early stage and to mitigate the risk that situations evolve towards dangerous incidents. Hereby, the crowd density is an important characteristic to assess the criticality of crowd situations.
Electronics
The surfer and the physical location are two important concepts associated with each other in the social network-based localization service. This work consists of studying urban behavior based on location-based social networks (LBSN) data; we focus especially on the detection of abnormal events. The proposed crowd detection system uses the geolocated social network provided by the Twitter application programming interface (API) to automatically detect the abnormal events. The methodology we propose consists of using an unsupervised competitive learning algorithm (self-organizing map (SOM)) and a density-based clustering method (density-based spatial clustering of applications with noise (DBCSAN)) to identify and detect crowds. The second stage is to build the entropy model to determine whether the detected crowds fit into the daily pattern with reference to a spatio-temporal entropy model, or whether they should be considered as evidence that something unusual occurs in the city bec...
2nd International Conference on Computer Applications & Information Security , 2019
Smart cities aim not only to make people's lives more enjoyable but also safer using advanced technology. Being in a crowded community spaces such as schools, colleges, stadiums, subway stations or holy spots on Pilgrimage impacts not only the level of human convenience but above all the threat of human security. An abnormal crowd conduct can lead to push, mass panic, stampede, crowd-crush, and causing an overall control loss. The current work introduced a mobile-based crowd abnormal behavior detection and management system. The system consists of two main parts; firstly, the server-side application connected to IP surveillance camera(s) to detect any abnormal crowd behavior and also crowd level in the entrance gates location(s), while the second main part is a mobile application with different users rights to receive an alarm from the server-side application in case of increasing crowd level or abnormal movement. The suggested framework provides an effective method to connect and alert all the system users immediately, preventing danger resulting from abnormal crowd behavior.
Context-Aware Crowd Monitoring with Dynamic Multi-User Tracking Data
International Journal of Engineering & Technology
Monitoring small crowd of people as tourists in different country always create recurrence issues to their tour-guides such as someone is lost somewhere, losing important documents and getting sick in the middle of the crowd. Similarly, during the Hajj season, such issues occur while millions of Muslims are gathered in two popular cities, Mecca and Medina, of Saudi Arabia. At the peak of the Hajj season, Mecca is identified as the most crowded place when pilgrims all over the world along with their respective tour-guides known as Mutawwif are resided in the city. Thus, communication between the crowd and their respective tour guides offers useful dynamic multi-user tracking data which is essential for close monitoring purposes. This study explores the usage feasibility of dynamic multi-user tracking data in order to provide a context-aware and simple communication means in the form of mobile application to both pilgrims and Mutawwifs for resolving their recurrence issues. The appl...