Mechanical response of dense pedestrian crowds to the crossing of intruders

Background: With the world's population projected to grow from the current 6.8 billion to around 9 billion by 2050, the resultant increase of megacities and the associated demands on public transport, there is an urgent imperative to understand the dynamics of crowded environments. Very dense crowds that exceed 3 people per square metre present many challenges for efficiently measuring quantities such as density and pedestrian trajectories. The Hajj and the associated minor Muslim pilgrimage of Umrah, present some of the most densely crowded human environments in the world, and thus present an excellent observational laboratory for the study of dense crowd dynamics. An accurate characterisation of such dense crowds cannot only improve existing models, but can also help to develop better intervention strategies for mitigating crowd disasters such as the Hajj 2006 Jamarat stampede that killed over 300 pilgrims. With Qatar set to be one of the cultural centres in the region, e.g. FIFA World Cup 2022, the proper control and management of large singular events is important for not only our safety but also our standing in the international stage. Objectives: To use the data gathered from the Hajj to assess the dynamics of large dense crowds with a particular focus on crowd instabilities and pattern formation. Methods: We will make use of advanced image processing and pattern recognition techniques (mathematical morphology, feature selection etc.) in assessing some of the bulk properties of crowds such as density and flow, as well as the finer details such as the ensemble of pedestrian trajectories. We are currently in the process of taking multiple wide-angle stereo videos at this year's Hajj, with our collaborators in Umm Al-Qurra University in Mecca. Multiple video capture of the same scene from different angles allows one to overcome the problem of occlusion in dense crowds. Results: We will present our field study in the Hajj this year, where we took extensive high quality multiple camera video data. We will also present some of the techniques, which we will be using over the coming year in analyzing this large data set that we have now successfully collated.

Crowd density, defined as persons per square meter, is a basic measuring unit for describing and analyzing crowd dynamics and for planning pedestrian infrastructure. However, little is known about the relationship between crowd density and psychological stress and well-being. This study uses an experimental approach to determine whether higher crowd densities result in higher levels of stress in participants. In this experiment, which was a case study at the university, participants (N = 29) wait in a wooden box of 1 m2 for three minutes. Two, four, six, or eight participants are present simultaneously in the box. It is varied whether participants are supposed to remain silent or to speak with each other. Stress is conceptualized as arousal and measured as skin conductance level/electrodermal activity (EDA). A questionnaire is administered after the experiment, and the positioning of participants in the box is videotaped. The results show that the correlation between crowd density and physiological arousal is more complex than expected. The specific social situation in the box appears to play a more important role than merely the number of people waiting there. Furthermore, our data indicate a temporal trend: participants seem to adapt to the crowd density in the box. Video data analysis reveals that participants choose their positioning and orientation in the box carefully, but that this social choreography works less smoothly in higher densities. This study shows promising results for using EDA as a measurement of arousal in the context of crowd research. However, the limitations of this method and the experiments conducted are also discussed in detail to further improve this approach.

Pedestrian tracking in dense crowds is a challenging task, even when using a multi-camera system. In this paper, a new Markov random field (MRF) model is proposed for the association of tracklet couplings. Equipped with a new potential function improvement method, this model can associate the small tracklet coupling segments caused by dense pedestrian crowds. The tracklet couplings in this paper are obtained through a data fusion method based on image mutual information. This method calculates the spatial relationships of tracklet pairs by integrating position and motion information, and adopts the human key point detection method for correction of the position data of incomplete and deviated detections in dense crowds. The MRF potential function improvement method for dense pedestrian scenes includes assimilation and extension processing, as well as a message selective belief propagation algorithm. The former enhances the information of the fragmented tracklets by means of a soft link with longer tracklets and expands through sharing to improve the potentials of the adjacent nodes, whereas the latter uses a message selection rule to prevent unreliable messages of fragmented tracklet couplings from being spread throughout the MRF network. With the help of the iterative belief propagation algorithm, the potentials of the model are improved to achieve valid association of the tracklet coupling fragments, such that dense pedestrians can be tracked more robustly. Modular experiments and system-level experiments are conducted using the PETS2009 experimental data set, where the experimental results reveal that the proposed method has superior tracking performance.

Empirical study of a unidirectional dense crowd during a real mass event

Towards crowd density-aware video surveillance applications

In designing urban spaces, crowd behaviour and their dynamics are usually overlooked. ?Crowd dynamics can be defined as the study of how and where crowds form and move? {1}. This paper aims to demonstrate how careful observation and analysis of crowd movement and the space. Crowd density and the response behaviour will vary for different nodes in a city. Crowd dynamics study help us to understand how crowd movement is affecting the space and how a crowd occupies the space. The morphological aspect has direct relation with people behaviour. Thus, crowd dynamics the result of a collective behaviour, which have impacts on physical setting. This is expected to be important for the shaping of urban morphology. The study is conducted by analyzing the urban form of the marketplace of the crowd interacting place of the city. The urban form characteristic i. e., the physical setting components of the market (here case Chalai Market) is analyzed to understand the interrelation with crowd dynamics. Thereby the study reaches at the conclusion by establishing the interrelation with the parameters identified on the pattern and density of crowd. The findings of this paper aid the place making process, which consider the aspects of cro wd dynamics.

We are interested in modeling and simulating the dynamics of human crowds, where the spreading of an emotion (specifically fear) influences the pedestrians’ behavior. Our focus is on crowd dynamics in venues where dense aggregations might occur within a rarefied crowd (e.g., an airport terminal) and emotional states evolve in space and time as the result of a threat (e.g., a gunshot). In the parts of the venue where crowd density is low, we consider a microscopic, individual-based model inspired by Newtonian mechanics. In this model, the fear level of each pedestrian influences their walking speed and is affected by the fear levels of the people in their vicinity. The mesoscopic model is derived from the microscopic model via a mean-field limit approach. This ensures that the two types of models are based on the same principles and analogous parameters. The mesoscopic model is adopted in the parts of the venue where crowd density is higher, i.e., we use the crowd density as a regime indicator. We propose interface conditions to be imposed at the boundary between the regions of the domain where microscopic and mesoscopic models are used. We note that we do not consider dangerously high-density crowd scenarios, for which a macroscopic (continuum) model would be more appropriate. We test our microscopic-to-mesoscopic model on problems involving a crowd walking through a corridor or evacuating from a square.

Movement of pedestrian crowds is ubiquitous in human society. However, it is unclear what dynamical regimes pedestrian crowds can exhibit at different crowd densities, how pedestrians move in these different dynamical regimes, and in which dynamical regime the movement synchronization of pedestrians is most likely to occur. Here, we conducted a unidirectional crowd movement experiment, in which we tracked the movement of pedestrian crowds through foot tracking. We find experimentally that pedestrian crowds can exhibit three distinct dynamical regimes (free regime, slow-moving regime, and jammed regime) depending on the crowd density. In the free regime, pedestrians' movement is not constrained; in the slow-moving regime, pedestrians' speed is constrained, but pedestrians' movement direction in each step is not influenced; and in the jammed regime, both pedestrians' speed and movement direction in each step are constrained. We also demonstrate that pedestrians are most likely to synchronize their movements spontaneously at the onset of jamming. Our findings provide important insights into crowd dynamics.

Reliable estimation of crowd density in public plays an important role on intelligent surveillance in recent years. There have been a lot of research on people counting; however, most of them only consider crowd with slight occlusions and their algorithms usually accompany with high computational complexity. In this paper, we present a simple model based on image potential energy to estimate the crowd density. The image potential energy is inspired by gravitational potential energy. Based on the facts that the pixels related to the object on the image plane are fewer if the object is farther away from the camera and the farther objects appear closer to the origin of the image plane, we define the image potential energy on the image plane. The main characteristics of the model is that the image potential energy related to objects is almost invariable no matter how far away the object being from the camera. The potential energy model can deal with severe occlusions with low computational complexity. It is adaptive to low and high density of crowd in public scenes. When the crowd density is below 10, the model accuracy rate is about 80% and the error is about 1 people count for a series of frames. When the crowd density varies from 10 to 40, the crowd density changes very fast, we can't make accuracy analysis as in low crowd density; however, for one single frame, the error rate is below 7% while the average error varies from 1 to 3 in the experiments.

Human observers are often relied upon for monitoring suspicious crowd behavior in both civilian and military contexts. However, little research has examined what individual- and crowd-level variables independently and interactively modulate threat perception among human observers. Five experiments gathered threat estimates while participants viewed static or dynamic crowd simulations. Experiments 1 and 2 used static crowd stimuli and manipulated crowd size (number of entities), crowd density (distance between entities), and historical information about adverse events. Experiments 3-5 used moving crowd stimuli and either fixed (Experiment 3) or dynamic (Experiment 4-5) crowd size and density. Experiments 4 and 5 further examined several individual- and crowd-level parameters subjectively reported by observers as critical to generating risk estimates. Overall, results demonstrated that human observers rely heavily on both crowd size and density cues, but also consider several other cues, such as perceived individual isolation and grouping behavior, when estimating risk levels within a crowd. We also show that reliance on such parameters is highly variable across participants in terms of both directionality and magnitude. Results are discussed within the context of continuing sensor system and modeling efforts, and understanding how threat perception emerges from the observation of intentional agents.

Dynamics of emotional contagion in dense pedestrian crowds

With the growth in world population, the density of crowds in public places has been increasing steadily, leading to a higher incidence of crowd disasters at high densities. Recent research suggests that emergent chaotic behavior at high densities-known collectively as crowd turbulence-is to blame. Thus, a deeper understanding of crowd turbulence is needed to facilitate efforts to prevent and plan for chaotic conditions in high-density crowds. However, it has been noted that existing algorithms modeling collision avoidance cannot faithfully simulate crowd turbulence. We hypothesize that simulation of crowd turbulence requires modeling of both collision avoidance and frictional forces arising from pedestrian interactions. Accordingly, we propose a model for turbulent crowd simulation, which incorporates a model for interpersonal stress and acceleration constraints similar to real-world pedestrians. Our simulated results demonstrate a close correspondence with observed metrics for crowd turbulence as measured in known crowd disasters.

Tracking in dense crowds using prominence and neighborhood motion concurrence

A fuzzy-theory-based method for studying the effect of information transmission on nonlinear crowd dispersion dynamics

Crowd management has been a topic of concern for many years because accidents frequently occur in situations with a high crowd density. With only a finite amount of space available during shows, protests, or other special occasions, a high crowd density can present a clear danger for those in the area. Considering these challenges, we employed and modified a three-tier multicolumn convolutional neural network (MCNN) system architecture to precisely estimate crowd density. We distinguished three regions from the near to far field to produce a crowd density map. Based on the MCNN system architecture, we detected changes in the size of a crowd according to a distance measure and examined additional features that can be incorporated to demonstrate their effects on crowd density maps. Examining these features using the Shanghaitech dataset demonstrated that compared with the native MCNN, the accuracy of estimating crowd counting by using our proposed method increased by 22.97% and 18.64% in terms of mean absolute error (MAE) and mean square error (MSE), respectively. A performance comparison with other state-of-the-art methods was also made. From this, we can infer that the proposed system is compatible with the other listed methods and is worthy of further investigation.