Table of contentsIntroductionAnalysis of crowd behavior during a mass gathering eventCrowd detectionModels for understanding crowd behaviorEstimating crowd densityLiterature on crowd behavior in crowd situations emergencyTheories of mass panicAffiliation and normative modelsSocial identity/self-categorization approachDisease spreadRisk involved with crowd eventsConclusionReferences:IntroductionIn denser scenes, it is very difficult to trace individual components within the crowd. However, as an extension of the current more general investigation of crowds, an additional aim was to think about crowd behavior specifically with reference to very large-scale, multi-day crowd events, specializing in the implications of study results for planning and management. these major events. The concepts of crowd, crowd mood, crowd type, and crowd behavior have various applications. The majority of research specializes in crowd behavior in the context of violence and conflict. More in-depth research on the concept of crowd would be enriched by more concrete definitions of conferences. Crowd type is an environmental descriptor of the demographic characteristics of a crowd. Crowd mood is derived from gang type and is more of a psychosocial descriptor of the crowd. Research has identified the importance of crowds in managing public safety during mass gatherings. The carnival began as an impromptu street procession between 1959 and 1964, aiming to bring area residents together after a series of race riots in the area in the late 1950s. Say no to plagiarism. Get a tailor-made essay on “Why violent video games should not be banned”?Get the original essayAnalysis of Crowd Behavior in a Mass Gathering EventCrowd DetectionDavie et al. (1995) proposed an approach to estimate gang density using a background subtraction removal and edge detection technique. Ma et al. (2004) developed a method that supports counting foreground objects on each pixel to estimate gang density. Kong et al. (2006) presented a method for estimating the number of individuals in a crowd. Object-level analysis attempts to spot individual objects within a scene. Zhao et al. (2003) presented a Baysian approach to segmenting people in crowds. Models for understanding crowd behaviorJacques et al. (2007) proposed an algorithm for group detection and crowd classification in voluntary or involuntary assisted computer vision. Cheriyadat et al. (2008) presented an approach to group a group of low-level motion features into trajectories. From the literature analysis, many researchers have focused on crowd density estimation, crowd tracking, and crowd behavior analysis supported by a holistic approach. there is a need for research into the effect of gender, age group, group size, carrying children, detaining children and other people with or without baggage on behavior of the crowd. Crowd Density Estimation Gang density estimation, background subtraction technique was used. The background subtraction approach is widely used to detect detected moving objects by taking care of the difference between the current frame and the coordinate system. Then the current image isconverted from RGB to gray and therefore the images are compared to find the difference. Later, the image is converted to binary and the blobs contained in the image are opened. BLOB – Binary Large Objects and it usually represents a group of pixels having similar but different intensity values ​​from those around it. The ratio between the number of crowds and the world gives the density of the crowd. Literature on Crowd Behavior in Emergency Theories of Mass Panic The notion of “mass panic” – the traditional panic model is typically used to describe crowd response to emergency situations. This theory, which builds on Le Bon's conceptualization that crowds are more emotional and less intelligent than individuals when acting alone, suggests that, in the face of an emergency or disaster, the social bonds between members of a community crowd dissolve, leading to mindless, instinctive, irrational, and self-centered behavior. Indeed, the classic panic entrapment theory proposes that when major physical danger is imminent but escape routes are limited. Affiliation and normative models Unlike the normal panic model, both affiliation and normative approaches emphasize that in an emergency or evacuation situation, crowd behaviors are not reduced to irrational and selfish tendencies but rather that the gang maintains its sociality. for example, studies of mass evacuations have shown that family groups do not disintegrate in an emergency, but plan to evacuate together and remain united as a group. People prefer to delay evacuation until everyone in the group is ready to leave together. However, the downside of this situation is often that families may also be slower to begin evacuation, which ultimately may threaten their survival. -is a model of emergent mass sociality and collective resilience, proposed to explain the collective sociality of crowds – behaviors of mutual aid, cooperation and coordination exhibited by individuals who do not know each other – in emergency situations. This model suggests that the common experience of threat or emergency can transform a physical crowd into a psychological crowd, with a shared social identity. Consistent with the principles of social identity theory and self-categorization theory, the way in which individuals understand their social identity - their self-concept defined in terms of specific group memberships, determined by method of categorization - depends not only on their knowledge of the groups, but also on the precise context and on the comparison. Spread of Disease In busy places, the fear of being crushed is not the only concern. Another concern is disease transmission. Although epidemiological processes are closely linked to foot traffic and transportation modes, the time scales are generally longer and the spatial extents larger. Epidemiological models generally operate at the population level rather than at the individual level. The advantage of working at a macroscopic level is that the scale of the problem does not become a limiting factor. The disadvantages are that interventions that can prevent the spread of the disease, for example: vaccination, testing, quarantine and travel restrictions for infected people, generally operate at a macroscopic level. The disease spreads more quickly in larger clusters, but is limited by the size of the cluster, which has important effects on the spread of the disease. There are