Controlling Individual Agents in High-Density Crowd Simulation

Simulating the motion of realistic, large, dense crowds of autonomous agents is still a challenge for the computer graphics community. Typical approaches either resemble particle simulations (where agents lack orientation controls) or are conservative in the range of human motion possible (agents lack psychological state and aren’t allowed to ‘push’ each other). Our HiDAC system (for High-Density Autonomous Crowds) focuses on the problem of simulating the local motion and global wayfinding behaviors of crowds moving in a natural manner within dynamically changing virtual environments. By applying a combination of psychological and geometrical rules with a social and physical forces model, HiDAC exhibits a wide variety of emergent behaviors from agent line formation to pushing behavior and its consequences; relative to the current situation, personalities of the individuals and perceived social density

Physical crowds and psychological crowds: applying self-categorization theory to computer simulation of collective behaviour

Computer models are used to simulate pedestrian behaviour for safety at mass events. Previous research has indicated differences between physical crowds of co-present individuals, and psychological crowds who mobilise collective behaviour through a shared social identity. This thesis aimed to examine the assumptions models use about crowds, conduct two studies of crowd movement to ascertain the behavioural signatures of psychological crowds, and implement these into a theoretically-driven model of crowd behaviour. A systematic review of crowd modelling literature is presented which explores the assumptions about crowd behaviour being used in current models. This review demonstrates that models portray the crowd as either an identical mass with no inter-personal connections, unique individuals with no connections to others, or as small groups within a crowd. Thus, no models have incorporated the role of self-categorisation theory needed to simulate collective behaviour. The empirical research in this thesis aimed to determine the behavioural effects of self-categorisation on pedestrian movement. Findings from a first study illustrate that, in comparison to a physical crowd, perception of shared social identities in the psychological crowd motivated participants to maintain close proximity with ingroup members through regulation of their speed and distance walked. A second study showed that collective self-organisation seemed to be increased by the presence of an outgroup, causing ingroup members to tighten formation to avoid splitting up. Finally, a computer model is presented which implements the quantified behavioural effects of self-categorisation found in the behavioural studies. A self-categorisation parameter is introduced to simulate ingroup members self-organising to remain together. This is compared to a physical crowd simulation with group identities absent. The results demonstrate that the self-categorisation parameter provides more accurate simulation of psychological crowd behaviour. Thus, it is argued that models should implement self-categorisation into simulations of psychological crowds to increase safety at mass events

Emergency Evacuation Software Model For Simulation Of Physical Changes

Public space such as schools, cinemas, shopping malls, etc. must have an emergency evacuation system in place. Such places are also required to follow certain regulations and protocols for emergency evacuation to assure the safety of their occupants inside from any unpredictable incident. For nearly two decades, companies/organizations are using simulation models/software for evacuation planning. Researchers are working on these software models to improve the efficiency using latest algorithms. This thesis focuses on creating a base software model of evacuation systems for 3D indoor environments to simulate physical changes such as retractable chairs, movable walls etc., to evaluate their effectiveness before committing to those changes. This research tries to address various flaws and shortcomings of previous software. We are using tools like Unity 3D and Autodesk Maya to simulate suggested changes. It provides planners as well as researchers a new perspective to work on new recommended physical changes to design public venues

Detection and Simulation of Dangerous Human Crowd Behavior

Tragically, gatherings of large human crowds quite often end in crowd disasters such as the recent catastrophe at the Loveparade 2010. In the past, research on pedestrian and crowd dynamics focused on simulation of pedestrian motion. As of yet, however, there does not exist any automatic system which can detect hazardous situations in crowds, thus helping to prevent these tragic incidents. In the thesis at hand, we analyze pedestrian behavior in large crowds and observe characteristic motion patterns. Based on our findings, we present a computer vision system that detects unusual events and critical situations from video streams and thus alarms security personnel in order to take necessary actions. We evaluate the system’s performance on synthetic, experimental as well as on real-world data. In particular, we show its effectiveness on the surveillance videos recorded at the Loveparade crowd stampede. Since our method is based on optical flow computations, it meets two crucial prerequisites in video surveillance: Firstly, it works in real-time and, secondly, the privacy of the people being monitored is preserved. In addition to that, we integrate the observed motion patterns into models for simulating pedestrian motion and show that the proposed simulation model produces realistic trajectories. We employ this model to simulate large human crowds and use techniques from computer graphics to render synthetic videos for further evaluation of our automatic video surveillance system

From mindless masses to small groups: Conceptualizing collective behavior in crowd modeling.

Computer simulations are increasingly used to monitor and predict behavior at large crowd events, such as mass gatherings, festivals and evacuations. We critically examine the crowd modeling literature and call for future simulations of crowd behavior to be based more closely on findings from current social psychological research. A systematic review was conducted on the crowd modeling literature (N = 140 articles) to identify the assumptions about crowd behavior that modelers use in their simulations. Articles were coded according to the way in which crowd structure was modeled. It was found that 2 broad types are used: mass approaches and small group approaches. However, neither the mass nor the small group approaches can accurately simulate the large collective behavior that has been found in extensive empirical research on crowd events. We argue that to model crowd behavior realistically, simulations must use methods which allow crowd members to identify with each other, as suggested by self-categorization theory

Modeling, Evaluation, and Scale on Artificial Pedestrians: A Literature Review

Modeling pedestrian dynamics and their implementation in a computer are challenging and important issues in the knowledge areas of transportation and computer simulation. The aim of this article is to provide a bibliographic outlook so that the reader may have quick access to the most relevant works related to this problem. We have used three main axes to organize the article's contents: pedestrian models, validation techniques, and multiscale approaches. The backbone of this work is the classification of existing pedestrian models; we have organized the works in the literature under five categories, according to the techniques used for implementing the operational level in each pedestrian model. Then the main existing validation methods, oriented to evaluate the behavioral quality of the simulation systems, are reviewed. Furthermore, we review the key issues that arise when facing multiscale pedestrian modeling, where we first focus on the behavioral scale (combinations of micro and macro pedestrian models) and second on the scale size (from individuals to crowds). The article begins by introducing the main characteristics of walking dynamics and its analysis tools and concludes with a discussion about the contributions that different knowledge fields can make in the near future to this exciting area

Visual Analysis of Videos of Crowded Scenes

Automatic, vision-based analysis of crowds has implications in a number of fields, but faces unique challenges due to the large number of pedestrians within the scenes. The movement of each pedestrian contributes to the overall crowd motion (i.e., the collective motions of the scene's constituents) that varies spatially across the frame and temporally over the video. This thesis explores how to model the dynamically varying crowd motion, and how to leverage it to perform vision-based analysis on videos of crowded scenes. The crowd motion serves as a scene-centric constraint (i.e., representing the motion in the entire video), compared with conventional objectcentric methods that build on individual constituents. By exploring what information the crowd motion can represent, we demonstrate the impact of leveraging our model on three problems facing video analysis of crowded scenes. First, we represent the crowd motion using a novel statistical model of local motion patterns (i.e., the motion in local space-time areas). By doing so, we may learn the spatially and temporally varying underlying structure of the crowd motion from an example video of crowd behavior. Second, we use our model to represent the typical crowd activity (i.e., the crowd's steady-state) and detect unusual events in local areas of the video. Specifically, we identify local motion patterns that statistically deviate from our learned model. Our space-time model enables detection and isolation of unusual events that are specific to the scene and the location within the video. Next, we use the crowd motion as an indicator of an individual's motion to perform tracking. Specifically, we predict the local motion patterns at different space-time locations of the video and use them as a prior to track individuals in a Bayesian framework. Leveraging the crowd motion provides an accurate prior that dynamically adapts to the space-time variations of the crowd. Finally, we explore how to measure how much individual pedestrians conform to the movement of the crowd. To achieve this, we use our crowd model to indicate the future locations of pedestrians, and compare the direction they would move to their instantaneous optical flow. By identifying deviations from the crowd, we identify global unusual events and augment our tracking method to model the individuality of each target. We compare with conventional object-centric methods and those that do not encode the space-time varying motion of the crowd. We demonstrate that our scene-centric approach (i.e, one that starts with the crowd motion) advances video analysis closer to the robustness and dependability needed for real-world video analysis of scenes containing a large number of pedestrians.Ph.D., Computer Science -- Drexel University, 201

Computational Study of Social Interactions and Collective Behavior During Human Emergency Egress.

Egress of occupants from a facility is normally straightforward. Problems arise when an emergency is present and many occupants are attempting to egress as quickly as possible, at which point egress can become life threatening. There are many reported events in history where emergency egress resulted in extensive loss of life and injuries. Egress research depends heavily on computational modeling because ethical and safety concerns preclude running experiments involving emergency crowd evacuations. However, to date, existing egress models rarely take into account meaningful social interactions and adherence to cultural norms, both of which are commonly present among egressing occupants and have significant influence on their egress response. The objective of this study is to develop a new methodology to address this gap using an Agent-Based computational platform. A novel method, termed Scalar Field Method (SFM), is proposed to accomplish this goal. The new technique draws on an analogy to a charged particle in an electromagnetic field to simulate the decision making process of an agent as it navigates through a facility and considers social interactions in its quest to egress. Two categories of social interactions are accounted for: 1) pre-existing social relationships associated with social identities, and 2) informal relations in collective behaviors such as lining up in counter-flow, queuing, and collective mobility. The latter is achieved by requiring an agent to establish informal and transient leader-follower relationships with others while adjusting its behavioral patterns as warranted by the situation. Simulation results demonstrate the model’s capabilities of handling social interactions, modeling reasonable egress behavior, and mimicking self-organized social gathering and collective behavior during egress. Comparisons with field studies show that the computational results correlate realistically with experimental data. A case study of the Station Nightclub fire that occurred in Rhode Island in 2003 and killed 100 occupants demonstrates that the proposed computational tools have strong potential for quantitatively exploring the influence of social level traits on egress situations.PhDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113381/1/calcite_1.pd

Integration of micro- and macroscopic models for pedestrian evacuation simulation

Simulation of pedestrian evacuations of smart buildings in emergency is a powerful tool for building analysis, dynamic evacuation planning and real-time response to the evolving state of evacuations. Macroscopic pedestrian models are low-complexity models that are and well suited to algorithmic analysis and planning, but are quite abstract. Microscopic simulation models allow for a high level of simulation detail but can be computationally intensive. By combining micro- and macro- models we can use each to overcome the shortcomings of the other and enable new capability and applications for pedestrian evacuation simulation that would not be possible with either alone. We develop the EvacSim multi-agent pedestrian simulator and procedurally generate macroscopic flow graph models of building space, integrating micro- and macroscopic approaches to simulation of the same emergency space. By “coupling” flow graph parameters to microscopic simulation results, the graph model captures some of the higher detail and fidelity of the complex microscopic simulation model. The coupled flow graph is used for analysis and prediction of the movement of pedestrians in the microscopic simulation, and investigate the performance of dynamic evacuation planning in simulated emergencies using a variety of strategies for allocation of macroscopic evacuation routes to microscopic pedestrian agents. The predictive capability of the coupled flow graph is exploited for the decomposition of microscopic simulation space into multiple future states in a scalable manner. By simulating multiple future states of the emergency in short time frames, this enables sensing strategy based on simulation scenario pattern matching which we show to achieve fast scenario matching, enabling rich, real-time feedback in emergencies in buildings with meagre sensing capabilities

Systematic Parameter Optimization and Application of Automated Tracking in Pedestrian-Dominant Situations

RÉSUMÉ Les mouvements des piétons et leur modélisation constituent un domaine de recherche de plus en plus actif. Bien qu’encore souvent appliqué à la sécurité par l’élaboration de plans d’évacuation en cas d’urgence, comprendre le mouvement des piétons est un enjeu économique de plus en plus important, notamment pour améliorer l’efficacité des aménagements de transport et des grands centres commerciaux. Cependant, les données existantes — particulièrement au niveau individuel, ou microscopique —sont majoritairement collectées dans des situations expérimentales contrôlées. Elles ne sont donc pas nécessairement représentatives du comportement des piétons dans des situations réelles, particulièrement en tenant compte de la susceptibilité de leur comportement aux facteurs démographiques, psychologiques et nvironnementaux. Cette lacune est due principalement à l’absence de méthodes prouvées pour la détection et le suivi de piétons dans des cas réels, absence qui résulte de la complexité des mouvements piétons et qui persiste malgré l’avancement continu des méthodes automatique d’analyse.----------ABSTRACT Though a wealth of data exists for the characterization of pedestrian movement, a majority of it originates from experimental settings owing to the current state of trackers for real-world scenarios. While these trackers are steadily improving, they remain insufficiently reliable for the accurate, microscopic tracking of individuals, particularly in cases of occlusion or higher density, complex scenes. In this work, the use of evolution algorithms is proposed for the systematic calibration of the parameters of existing trackers in order to further optimize their performance – evaluated by tracking accuracy and precision metrics – in complex cases, with an initial focus on two tracking methods designed for multimodal analysis. This calibration is further aided by the inclusion of additional parameters regulating homography, or specifically the plane to which tracker detections are projected. Three real test cases were used: a) a confined corridor in a public building, b) a subway station entrance during morning rush hour and c) a crosswalk in downtown New York. Results demonstrate a halving of tracking errors over both default and manually-calibrated parameters, as well as a strong correlation in performance between similar cases. These results were consistent over multiple trials and regardless of the starting parameters, strongly implying that the obtained solutions are indeed the global maxima for each scene. For application and validation of the resultant tracks, flow characterization and directional counting are demonstrated, utilizing tools included in the optimization framework