Simulating group formation and behaviour in dense crowd

Nasir, Fawwaz Mohd; Noma, Tsukasa; Oshita, Masaki; Yamamoto, Kunio; Sunar, Mohd Shahrizal; Mohamad, Shamsul; Honda, Yasutaka

doi:10.1145/3013971.3014017

Cited by 8 publications

(10 citation statements)

References 7 publications

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“…Majority of the available techniques detailing crowd simulation do not consider the mixed and heterogeneous behaviour of the crowd during Tawaf ritual, while crowd simulation applications are finding wider use in computer games. Nasir et al (2016) proposed a method that can simulate the formation of large groups in congested and dense crowd as each group tries to avoid individuals of other groups in a continuous manner to reach target destination. The "leader-follower" model mandates that members of a group follow their leader, who is responsible to determine the required path for undertaking any ritual.…”

Section: Simulation Using Leader-follower Modelmentioning

confidence: 99%

“…Moreover, this simulation model is also suitable for individual pilgrims, while changes in group formation caused by changing crowd density can also be simulated with the help of this model (Sarmady et al, 2011). A machine-learning or path-planning technique can be helpful to modify this model if the pilgrim group or groups desire to change their path in view of the changing crowd density (Nasir et al, 2016).…”

Section: Simulation Using Leader-follower Modelmentioning

confidence: 99%

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Simulation of crowd management using deep learning algorithm

Nafea

2021

IJWIS

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Purpose This study aims to propose a new simulation approach for a real-life large and complex crowd management which takes into account deep learning algorithm. Moreover, the proposed model also determines the crowd level and also sends an alarm to avoid the crowd from exceeding its limit. Also, the model estimates crowd density in the pictures through which the study evaluates the deep learning algorithm approach to address the problem of crowd congestion. Furthermore, the suggested model comprises of two main components. The first takes the images of the moving crowd and classifies them into five categories such as “heavily crowded, crowded, semi-crowded, light crowded and normal,” whereas the second one comprises of colour warnings (five). The colour of these lights depends upon the results of the process of classification. The paper is structured as follows. Section 2 describes the theoretical background; Section 3 suggests the proposed approach followed by convolutional neural network (CNN) algorithm in Section 4. Sections 5 and 6 explain the data set and parameters as well as modelling network. Experiment, results and simulation evaluation are explained in Sections 7 and 8. Finally, this paper ends with conclusion which is Section 9 of this paper. Design/methodology/approach This paper addresses the issue of large-scale crowd management by exploiting the techniques and algorithms of simulation and deep learning. It focuses on a real-life case study of Hajj pilgrimage in Saudi Arabia that exhibits intricate pattern of crowd management. Hajj pilgrimage includes performing Umrah along with hajj that involves several steps which is a sacred prayer of Muslims performed at different time span of the year. Muslims from all over the world visit the holy city of Mecca to perform Tawaf that is one of the stages included in the performance of Hajj or Umrah, it is an obligatory step in prayer. Accordingly, all pilgrims require visiting Mataf to perform Tawaf. It is essential to control the crowd performing Tawaf systematically in a constrained place to avoid any mishap. This study proposed a model for crowd management system by using image classification and a system of alarm to manage millions of people during Hajj. This proposed system highly depends on the adequate data set used to train CNN which is a deep learning technique and has recently drawn the attention of the research community as well as the industry in changing applications of image classification and the recognition of speed. The purpose is to train the model with mapped image data, making it available to be used in classifying the crowd into five categories like crowded, heavily crowded, semi-crowded, normal and light-crowded. The results produce adequate signals as they prove to be helpful in terms of monitoring the pilgrims which shows its usefulness. Findings After the first attempt of adding the first convolutional layer with 32 filters, the accuracy is not good and stands out at about 55%. Therefore, the algorithm is further improved by adding the second layer with 64 filters. This attempt is a success as it gives more improved results with an accuracy of 97%. After using the dropout fraction as a 0.5 to prevent overfitting, the test and training accuracy of 98% is achieved which is acceptable training and testing accuracy. Originality/value This study has proposed a model to solve the problem related to estimation of the level of congestion to avoid any accidents from happening because of it. This can be applied to the monitoring schemes that are used during Hajj, especially in crowd management during Tawaf. The model works as such that it activates an alarm when the default crowd limit exceeds. In this way, chances of the crowd reaching a dangerous level are reduced which minimizes the potential accidents that might take place. The model has a traffic light system, the appearance of red light means that the number of pilgrims in a particular area has exceeded its default limit and then it alerts to stop the migration of people to that particular area. The yellow light indicates that the number of pilgrims entering and leaving a particular area has equalized, then the pilgrims are suggested to slower their pace. Finally, the green light shows that the level of the crowd in a particular area is low and that the pilgrims can move freely in that area. The proposed model is simple and user friendly as it uses the most common traffic light system which makes it easier for the pilgrims to understand and follow accordingly.

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Section: Simulation Using Leader-follower Modelmentioning

confidence: 99%

Section: Simulation Using Leader-follower Modelmentioning

confidence: 99%

Simulation of crowd management using deep learning algorithm

Nafea

2021

IJWIS

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“…The simulations' results showed that the presented algorithm was effective when obvious flow patterns were shown within the crowd and could also cluster agents according to their long-term interests. In 2016, Nasir et al introduced a technique for the simulation of large groups in dense crowds (Nasir et al, 2016), focusing on the change of the groups' formation for avoiding collisions and maintaining the agents' collective behavior. They used a leader-followed methodology, a modified SFM, to maintain the group's formation (type) and agent density.…”

Section: Group Dynamicsmentioning

confidence: 99%

Crowd simulation for crisis management: The outcomes of the last decade

Sidiropoulos

Kiourt

Moussiades

2020

Machine Learning with Applications

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“…In addition, many other approaches and models for formation exist, such as the social‐force model (SFM), velocity‐based model, synthetic vision‐based approaches, and physics‐based methods. Nasir et al, for instance, used the leader–follower model, driven by the modified SFM. The SFM is one of the most popular and effective model to simulate crowds.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical planning‐based crowd formation

Liu

Wang

Liu

et al. 2019

Computer Animation & Virtual

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Team formation with realistic crowd simulation behavior is a challenge in computer graphics, multiagent control, and social simulation. In this study, we propose a framework of crowd formation via hierarchical planning, which includes cooperative-task, coordinated-behavior, and action-control planning. In cooperative-task planning, we improve the grid potential field to achieve global path planning for a team. In coordinated-behavior planning, we propose a time-space table to arrange behavior scheduling for a movement. In action-control planning, we combine the gaze-movement angle model and fuzzy logic control to achieve agent action. Our method has several advantages. (1) The hierarchical architecture is guaranteed to match the human decision process from high to low intelligence. (2) The agent plans his behavior only with the local information of his neighbor; the global intelligence of the group emerges from these local interactions.(3) The time-space table fully utilizes three-dimensional information. Our method is verified using crowds of various densities, from sparse to dense, employing quantitative performance measures. The approach is independent of the simulation model and can be extended to other crowd simulation tasks.

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Simulating group formation and behaviour in dense crowd

Cited by 8 publications

References 7 publications

Simulation of crowd management using deep learning algorithm

Simulation of crowd management using deep learning algorithm

Crowd simulation for crisis management: The outcomes of the last decade

Hierarchical planning‐based crowd formation

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