Crowds: Unpredictable Behavior of Large Human Gatherings

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About this sample


3 pages /

1209 words

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3 pages /

1209 words

Downloads: 14

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Table of contents

  1. Introduction
  2. Crowd behavioural analysis at a mass gathering event
  3. Crowd Detection
    Crowd Behaviour Understanding Models
    Crowd Density Estimation
  4. Literature on crowd behaviours in emergencies
  5. Theories of mass panic
    Affiliation and Normative Models
    Social Identity/Self-Categorization Approach
  6. Spread of diseases
  7. Risk involved with crowd events
  8. Conclusion
  9. References:


In denser scenes it's very difficult to trace the individual components within the crowd. However, as an extension to the present more general investigation of crowds, an extra aim was to think about crowd behavior specifically in reference to very large scale, multi-day crowd events, that specialize in the implications of the review findings for planning and managing these major events. The notions of crowd, crowd mood, crowd type, and crowd behavior have variety of applications. A majority of research specialise in crowd behavior in context of violence and conflict. Further research on the concept of crowd would be enhanced by more concrete definitions of the colloquia. Crowd type is an environmental descriptor of the demographics of a crowd. Crowd mood hails from the gang type and is more of a psychosocial descriptor of crowd. Research has identified the importance of crowds within the management of public safety at mass gatherings. The carnival first began as an off-the-cuff street procession between 1959 and 1964 as how of bringing the area people together after a series of race riots within the area within the late 1950s.

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Crowd behavioural analysis at a mass gathering event

Crowd Detection

Davie et al. (1995) proposed an approach to estimate the gang density by background subtraction removal technique and edge detection. Ma et al. (2004) developed a way supported the counting of foreground objects on each pixel to estimate the gang density. Kong et al. (2006) presented a way to estimate the amount of individuals during a crowd. Object level analysis tries to spot individual objects during a scene. Zhao et al. (2003) presented Baysian approach to segment people in crowds.

Crowd Behaviour Understanding Models

Jacques et al. (2007) proposed an algorithm for group detection and crowd classification as voluntary or involuntary supported computer vision. Cheriyadat et al. (2008) presented an approach for clustering a group of low-level motion features into trajectories. From the literature review, many researchers have focused on crowd density estimation, crowd tracking, and crowd behavior analysis supported holistic approach. there's a requirement to conduct research on effect of gender, age group, group size, child carrying, child holding and other people with and without luggage on crowd behavior.

Crowd Density Estimation

The estimation of the gang density, background subtraction technique was used. Background subtraction approach is broadly used for detecting moving objects detected supported difference between the present frame and coordinate system. Then current frame is converted from RGB to gray, and therefore the images are compared to seek out the difference. Later the image is converted into binary and blobs within the image are opened. BLOB – Binary Large Objects and it's wont to represent a gaggle of pixels having similar values for intensity but different from those surrounding it. The ratio of the crowd count to the world gives the crowd density.

Literature on crowd behaviours in emergencies

Theories of mass panic

The notion of ‘mass panic’ – the traditional panic model is usually wont to describe the crowd’s response to emergency situations. This theory-drawing on Le Bon’s conceptualization of crowds as more emotional and fewer intelligent than individuals when acting alone – suggests that, when faces with an emergency or disaster situation, the social bonds between members of a crowd dissolve, leading to mindless, instinctive, irrational and self- centred behavior

Indeed, the classic entrapment theory of panic proposes that when major physical danger is imminent but escape routes are limited.

Affiliation and Normative Models

In contrast to the normal model of panic, both affiliation and normative approaches stress that in an emergency situation or evacuation, crowd behaviors aren't reduces to irrational, selfish tendencies but rather that the gang retains its sociality. for example, studies of mass evacuation have found that family groups don't break down in an emergency, but plan to evacuate together and remain united as a gaggle. People like better to delay evacuating until all members of the group are ready to leave together. However, the drawbackof this is often that families could also be slower to start evacuation which, ultimately,can threaten their survival.

Social Identity/Self-Categorization Approach

In contrast the social identity/self categorization approach supported both social identity theory and self-categorization theory-is a model of mass emergent sociality and collective resilience, offered in explanation of the collective sociality of crowds- helping, cooperation and coordination behaviors displayed by individuals who don't know one another – in emergency situations.

This model suggests that the common experience of threat or emergency may 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 during which individuals understand their social identity- their self understanding defined in terms of specific group memberships, determined by the method of categorization- depends not only on their knowledge of the group, but also on the precise context and on comparison.

Spread of diseases

In crowded places, fear of being crushed is not the only concern. Another worry is the transmission of disease. Even though epidemiological processes are closely related to pedestrian crowding and modes of transport, the timescales are typically longer and spatial extents are larger. Epidemiological models typically operate on a population level rather than on an individual level. The advantage of working at a macroscopic level is that the scale of the problem does not become a restricting factor. Disadvantages are that the interventions that can prevent the spread of disease eg: immunization, screening, quarantine, and travel restrictions for infected individuals typically operate on macroscopic level. Disease will spread faster in the largest clusters but are restricted by the cluster size, which has important effects on the spread of disease. Many different models for the spread of epidemics exist, but one of the simplest and most well known is susceptible-infected-recovered, first proposed by kermack and McKendrick.

Risk involved with crowd events

Insufficient pre-planning, resulting in a lack of awareness and through consideration of what can go wrong and how it can be managed. Insufficient control system overseeing the whole event, with parties unaware of who is in charge. Lack of experienced personnel and lack of familiarity with the event environment. External risks such as terrorism and severe weather conditions. Crowd collapse, crushing and serious injury. Panic- when a large number of people in a small space sense something is wrong it can be lead to crushing, pushing and crowd collapse.

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In addition to the advantages of a microscopic epidemic spreading model, the disadvantages include the large computational burden for city-level or wider spatial application, and fragility of a complex model that has many variables. The literature on psychological factors of crowds high-lights a large theory practice gap in the setting of mass gatherings. This is further hampered by the language and context in which crowds have been studied. This literature review has revealed that there are two important elements of crowd behavior in mass gatherings: (1) for crowd behavior to change, there must be a seed, divergence from normal behavior; and (2) that people must engage in the aberrant behavior. This provides three opportunities to manage crowds through: (1) assessment and monitoring in the both the pre-event and during event phase; (2) identification and management of seed behavior; and (3) containment of crowd engagement. Crowd simulations are used to replicate the existing ground conditions and thereby create virtual environments for efficient planning and management of crowd evacuation under unforeseen situation.


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Crowds: Unpredictable Behavior Of Large Human Gatherings. (2022, February 10). GradesFixer. Retrieved September 21, 2023, from
“Crowds: Unpredictable Behavior Of Large Human Gatherings.” GradesFixer, 10 Feb. 2022,
Crowds: Unpredictable Behavior Of Large Human Gatherings. [online]. Available at: <> [Accessed 21 Sept. 2023].
Crowds: Unpredictable Behavior Of Large Human Gatherings [Internet]. GradesFixer. 2022 Feb 10 [cited 2023 Sept 21]. Available from:
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