In-Depth Survey to Detect, Monitor and Manage Crowd (original) (raw)
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Safety Science, 2017
Recent accidents [News, 2006, 2010, 2013, 2015] show that crowded events can quickly turn into tragedies. The goal of crowd management is to avoid such accidents through careful planning and implementation. Crowd management practices are collaborative efforts between the different actors of the crowd management team and the crowd that depend on effective handling, sharing, and communication of information. Safety and comfort of a crowd depend on the success of such efforts. We have studied current practices and the role of technology through interviews to crowd managers. Our findings show that event planning and monitoring can be complex and sophisticated, but are operated with little support from technology. Crowd managers intend to increase their use of technology, but they have been so far dissatisfied by existing solutions. We provide recommendations for a bigger role of technology in crowd management.
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.
Recent trends in crowd analysis: A review
Machine Learning with Applications, 2021
When overpopulated cities face frequent crowded events like strikes, demonstrations, parades or other sorts of people gatherings, they are confronted to multiple security issues. To mitigate these issues, security forces are often involved to monitor the gatherings and to ensure the security of their participants. However, when access to technology is limited, the security forces can quickly become overwhelmed. Fortunately, more and more important smart cities are adopting the concept of intelligent surveillance systems. In these situations, intelligent surveillance systems require the most advanced techniques of crowd analysis to monitor crowd events properly. In this review, we explore various studies related to crowd analysis. Crowd analysis is commonly broken down into two major branches: crowd statistics and crowd behavior analysis. When crowd statistics determines the Level Of Service (LoS) of a crowded scene, crowd behavior analysis describes the motion patterns and the activities that are observed in a scene. One of the hottest topics of crowd analysis is anomaly detection. Although a unanimous definition of anomaly has not yet been met, each of crowd analysis subtopics can be subjected to abnormality. The purpose of our review is to find subareas, in crowd analysis, that are still unexplored or that seem to be rarely addressed through the prism of Deep Learning.
Crowd Monitoring: State-of-the-Art and Future Directions
IETE Technical Review, 2020
With the growing concerns over public safety, the importance of crowd monitoring is being realized by various security and event management agencies worldwide. Estimation of crowd dynamics can help such agencies in prevention of any unanticipated accidents or issues. Research on crowd monitoring has been underway since the past few decades. Conventional crowd monitoring systems mainly rely on computer vision approach. Due to predominant use of videos/ image sequences, the existing techniques may raise data privacy concerns. This has led to development of new crowd monitoring techniques which are privacy preserving and require minimum public participation. This paper aims to serve as a single and sufficient source of information to the concerned researchers on various aspects of crowd monitoring and also provide future directions which can be helpful in developing advanced crowd monitoring techniques.
Lecture Notes in Computer Science, 2015
Festivals and big scale events are becoming more and more popular, they can attract thousands of spectators. Ensuring the safety of the crowd has become a top priority to many organisers after the multitude of dramatic accidents that resulted in losses in human lives. Monitoring the crowd via smartphones is a relatively new technique that emerged recently with the capabilities of mobile phones to transmit their GPS location data. We present a novel approach, based on the local crowd pressure, combined with the detection of groups in a crowd, to detect critical situations and propose evacuation plans that does not separate groups of people that are together. Groups were detected using DBSCAN clustering algorithm with 80 % accuracy. Location acquisition was tested during the Campus Fever event, and 87 % of the collected data had an accuracy lower than 10 m while 29 % of the total data had 5 m of accuracy. During 2 h of monitoring, activity of the application, reduced the battery of 20 %.
ecent trends in crowd analysis: A review
2021
When overpopulated cities face frequent crowded events like strikes, demonstrations, parades or other sorts of people gatherings, they are confronted to multiple security issues. To mitigate these issues, security forces are often involved to monitor the gatherings and to ensure the security of their participants. However, when access to technology is limited, the security forces can quickly become overwhelmed. Fortunately, more and more important smart cities are adopting the concept of intelligent surveillance systems. In these situations, intelligent surveillance systems require the most advanced techniques of crowd analysis to monitor crowd events properly. In this review, we explore various studies related to crowd analysis. Crowd analysis is commonly broken down into two major branches: crowd statistics and crowd behavior analysis. When crowd statistics determines the Level Of Service (LoS) of a crowded scene, crowd behavior analysis describes the motion patterns and the activ...
Crowd Management, 2019
The first text to present a system for crowd management which integrates security with the other concerns for the health and safety for crowds, looking at the theories and practices of the management processes, plans, monitoring and evaluation of crowds.
Machine Vision and Applications, 2008
In the year 1999 the world population reached 6 billion, doubling the previous census estimate of 1960. Recently, the United States Census Bureau issued a revised forecast for world population showing a projected growth to 9.4 billion by 2050 (US Census Bureau, http://www.census.gov/ipc/www/worldpop.html). Different research disci- plines have studied the crowd phenomenon and its dynamics from a social, psychological and computational standpoint respectively. This paper presents a survey on crowd analysis methods employed in computer vision research and discusses perspectives from other research disciplines and how they can contribute to the computer vision approach.
Crowd management and the use of technology to make or to mar
Adaeze Anthony , 2023
Crowd management is fast becoming a fundamental part of any discussion on event management. The need to make the world a global social village has given rise to events nationally and across national boundaries and cultures. With this responsibility comes the need to ensure the safety and overall well-being of every guest. Previous traditional methods have proven inadequate, thereby allowing more sophisticated modern technologies to come to the rescue. This paper would show that technology in crowd management has been immensely productive, but there is a dire need for collaboration between technology and human personnel in ensuring effective crowd management.
Crowd Analysis and Its Applications
Communications in Computer and Information Science, 2011
Crowd is a unique group of individual or something involves community or society. The phenomena of the crowd are very familiar in a variety of research discipline such as sociology, civil and physic. Nowadays, it becomes the most active-oriented research and trendy topic in computer vision. Traditionally, three processing steps involve in crowd analysis, and these include pre-processing, object detection and event/behavior recognition. Meanwhile, the common process for analysis in video sequence of crowd information extraction consists of Pre-Processing, Object Tracking, and Event/Behavior Recognition. In terms of behavior detection, the crowd density estimation, crowd motion detection, crowd tracking and crowd behavior recognition are adopted. In this paper, we give the general framework and taxonomy of pattern in detecting abnormal behavior in a crowd scene. This study presents the state of art of crowd analysis, taxonomy of the common approach of the crowd analysis and it can be useful to researchers and would serve as a good introduction related to the field undertaken.