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Simulating Crowd Interactions in Virtual Environments (Doctoral Consortium) Sujeong Kim * Advisors: Ming C. Lin and Dinesh Manocha University of North Carolina at Chapel Hill Index Terms: I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism—Animation; 1 I NTRODUCTION Understanding and modeling how a crowd behaves in a wide variety of situations is an important problem in many areas. For example, during the planning stages, city, traffic, and evacuation engineers use crowd behavior modeling to predict usage patterns and to do safety analysis of their designs. Several research areas benefit from realistic simulation of crowds such as augmented reality, animation, games, virtual therapy, and virtual training. Not only is a realistic rendering of a virtual environment required for these applications, but also a realistic simulation of virtual humans is essential to pro- viding an immersive experience for the users. Currently, it is very challenging to simulate realistic crowd be- haviors in virtual environments. First of all, there is no single com- plete model that can describe all the aspects of crowd behaviors. Psychologists and social scientists have proposed many different explanations about human behavior. Biomechanics have studied how physical conditions can affect human behavior. When model- ing crowds, we have to come up with a method that takes account such physical and psychological variabilities and conditions. Another difficulty is simulating an individual’s behavior when they interact with a crowd. The range and effect of an individual’s behavior can vary depending on if they are in a crowd or alone. For example, when multiple people cross a street, they will sometimes take a detour to avoid bumping into others. This means an individ- ual’s movement needs to be adjusted when interacting with others. In a dense crowd, if a person pushes someone by mistake, the push- ing force might be propagated through the crowd, causing domino effect. Thus, an individual’s behavior can also affect overall crowd behavior. Last but not least, the simulation should be stable and efficient. We often need to simulate dense, sometimes even massive (thou- sands to tens of thousands of agents in) crowds. The simulation should not fail in such cases, and be efficient enough to run at inter- active rates to be used for games or virtual reality applications. Many approaches have been proposed to address these chal- lenges in various ways. Some techniques use well-established the- ories from psychology [1, 3] or biomechanics [2]. Some tech- niques use database of example behaviors such as motion capture data [8], or directly design a motion model from real-world ex- periments [10]. Some other techniques use machine learning and computer vision techniques to learn behavior patterns from the real world video data [9, 11]. The goal of my research is to provide an interactive simulation of a crowd, where large numbers of human-like agents interact with each other. A large part of my research comes from understanding * e-mail: [email protected] and observing what human-like behavior is and how it affects inter- actions with others and the environment. In this proposal, I discuss my approaches to model crowd behaviors with variation and dy- namic changes. 2 RESEARCH SUMMARY In this section, I discuss my previous work focusing on increasing realism for both individual agents and overall crowd behavior. 2.1 Modeling Dynamic, Heterogeneous Behaviors In this work, I propose a method to model the variability and the dy- namic changes of individual behavior based on understandings of psychology studies. The proposed method is based on Attribution theory that finds a cause of a behavior from dispositional attributes and situational attributes. Dispositional attributes are internal fac- tors such as personality, and situational attributes are external fac- tors such as stimulus from current situation [4]. As a first step, Guy et al. [3] proposed a mapping between sim- ulation parameters and personality space of the virtual agents, ac- quired from a user study. This enables us to simulate agents with different personalities. In the following work, I propose a model that takes account situational stimuli [7]. The agents respond dy- namically to the stimuli from their situation. Figure 1: Two opposing groups approach each other. Agents ini- tially form natural lanes (left). Under stressful condition, such as the emergency alarm, the lane formation breaks down into uncoop- erative, clogged and congested behavior (right) [7]. Fig. 1 shows one of the results. We can see the crowd behav- ior change dynamically, from a stress-free environment where the crowd navigates in an orderly fashion, to a stressful environment where the crowd is chaotic. The wide variety of such dynamic behaviors are modeled by a uniform motion model incorporating personality and situational differences. More details and results are available on http://gamma.cs.unc.edu/GAScrowd/. 2.2 Learning Behavior Patterns from Real World Crowd In my following work, I attempt to fit the crowd motion model as close as possible to a given real-world example [5]. I propose an online, individualized, and adaptive system that learns behav- ior patterns from the real world trajectories and evolves itself as it gets new observation. The system uses a combination of Ensemble Kalman filter and maximum likelihood estimation algorithms, that work with a velocity-based multi-agent simulation technique as a motion model. 135 IEEE Virtual Reality 2014 29 March - 2 April, Minneapolis, Minnesota, USA 978-1-4799-2871-2/14/$31.00 ©2014 IEEE

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Page 1: [IEEE 2014 IEEE Virtual Reality (VR) - Minneapolis, MN, USA (2014.03.29-2014.04.2)] 2014 IEEE Virtual Reality (VR) - Simulating crowd interactions in virtual environments (doctoral

Simulating Crowd Interactions in Virtual Environments(Doctoral Consortium)

Sujeong Kim∗

Advisors: Ming C. Lin and Dinesh ManochaUniversity of North Carolina at Chapel Hill

Index Terms: I.3.7 [Computer Graphics]: Three-DimensionalGraphics and Realism—Animation;

1 INTRODUCTION

Understanding and modeling how a crowd behaves in a wide varietyof situations is an important problem in many areas. For example,during the planning stages, city, traffic, and evacuation engineersuse crowd behavior modeling to predict usage patterns and to dosafety analysis of their designs. Several research areas benefit fromrealistic simulation of crowds such as augmented reality, animation,games, virtual therapy, and virtual training. Not only is a realisticrendering of a virtual environment required for these applications,but also a realistic simulation of virtual humans is essential to pro-viding an immersive experience for the users.

Currently, it is very challenging to simulate realistic crowd be-haviors in virtual environments. First of all, there is no single com-plete model that can describe all the aspects of crowd behaviors.Psychologists and social scientists have proposed many differentexplanations about human behavior. Biomechanics have studiedhow physical conditions can affect human behavior. When model-ing crowds, we have to come up with a method that takes accountsuch physical and psychological variabilities and conditions.

Another difficulty is simulating an individual’s behavior whenthey interact with a crowd. The range and effect of an individual’sbehavior can vary depending on if they are in a crowd or alone. Forexample, when multiple people cross a street, they will sometimestake a detour to avoid bumping into others. This means an individ-ual’s movement needs to be adjusted when interacting with others.In a dense crowd, if a person pushes someone by mistake, the push-ing force might be propagated through the crowd, causing dominoeffect. Thus, an individual’s behavior can also affect overall crowdbehavior.

Last but not least, the simulation should be stable and efficient.We often need to simulate dense, sometimes even massive (thou-sands to tens of thousands of agents in) crowds. The simulationshould not fail in such cases, and be efficient enough to run at inter-active rates to be used for games or virtual reality applications.

Many approaches have been proposed to address these chal-lenges in various ways. Some techniques use well-established the-ories from psychology [1, 3] or biomechanics [2]. Some tech-niques use database of example behaviors such as motion capturedata [8], or directly design a motion model from real-world ex-periments [10]. Some other techniques use machine learning andcomputer vision techniques to learn behavior patterns from the realworld video data [9, 11].

The goal of my research is to provide an interactive simulationof a crowd, where large numbers of human-like agents interact witheach other. A large part of my research comes from understanding

∗e-mail: [email protected]

and observing what human-like behavior is and how it affects inter-actions with others and the environment. In this proposal, I discussmy approaches to model crowd behaviors with variation and dy-namic changes.

2 RESEARCH SUMMARY

In this section, I discuss my previous work focusing on increasingrealism for both individual agents and overall crowd behavior.

2.1 Modeling Dynamic, Heterogeneous Behaviors

In this work, I propose a method to model the variability and the dy-namic changes of individual behavior based on understandings ofpsychology studies. The proposed method is based on Attributiontheory that finds a cause of a behavior from dispositional attributesand situational attributes. Dispositional attributes are internal fac-tors such as personality, and situational attributes are external fac-tors such as stimulus from current situation [4].

As a first step, Guy et al. [3] proposed a mapping between sim-ulation parameters and personality space of the virtual agents, ac-quired from a user study. This enables us to simulate agents withdifferent personalities. In the following work, I propose a modelthat takes account situational stimuli [7]. The agents respond dy-namically to the stimuli from their situation.

Figure 1: Two opposing groups approach each other. Agents ini-tially form natural lanes (left). Under stressful condition, such asthe emergency alarm, the lane formation breaks down into uncoop-erative, clogged and congested behavior (right) [7].

Fig. 1 shows one of the results. We can see the crowd behav-ior change dynamically, from a stress-free environment where thecrowd navigates in an orderly fashion, to a stressful environmentwhere the crowd is chaotic. The wide variety of such dynamicbehaviors are modeled by a uniform motion model incorporatingpersonality and situational differences. More details and results areavailable on http://gamma.cs.unc.edu/GAScrowd/.

2.2 Learning Behavior Patterns from Real World Crowd

In my following work, I attempt to fit the crowd motion modelas close as possible to a given real-world example [5]. I proposean online, individualized, and adaptive system that learns behav-ior patterns from the real world trajectories and evolves itself as itgets new observation. The system uses a combination of EnsembleKalman filter and maximum likelihood estimation algorithms, thatwork with a velocity-based multi-agent simulation technique as amotion model.

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IEEE Virtual Reality 201429 March - 2 April, Minneapolis, Minnesota, USA978-1-4799-2871-2/14/$31.00 ©2014 IEEE

Page 2: [IEEE 2014 IEEE Virtual Reality (VR) - Minneapolis, MN, USA (2014.03.29-2014.04.2)] 2014 IEEE Virtual Reality (VR) - Simulating crowd interactions in virtual environments (doctoral

The proposed approach has unique properties and benefits com-pared to prior techniques. First, it can learn unique characteristicsof each individual. Prior techniques find global, uniform parame-ters for everybody in the entire sequence of data. Instead, the pro-posed approach learns time-varying parameters for each individualwith their own characteristics. Second, the method adapts itself tothe new observations. Thus, the method provides more accurate lo-cal prediction as well as increased overall accuracy for the entiresequence. Finally, the method does not need any prior knowledgeabout the scene and it can be used to learn and predict the behaviorsin online manner. Note that the prior approaches require manualwork or preprocessing of the data to find global parameters. Theproposed method performs very well with varying number of indi-viduals, varying noise level, varying density condition, and sparselysampled data. More results are available on the project websitehttp://gamma.cs.unc.edu/BRVO/.

2.3 Modeling Physical Interactions

Most of the multi-agent simulations focus mainly on local collisionavoidance. However, very little attention was given to the behaviorof an agent when collision actually happens, though such situationis observed frequently in real world. We can easily imagine somepeople who are pushed by somebody in a hurry, for example, in acrowded subway station. Moreover, many other interactions are ini-tiated by contact, that is by physically touching or applying forcesto a person or an object.

I propose a method that models such physical interactions be-tween agents or between agents and obstacles, while preservingthe ability of anticipated collision avoidance [6]. I introduce newmodels for interaction forces, which can capture human-like be-haviors that are different from the rigid-body motion. For example,the method can generate balance-recovery motion of the agents, ora propagation of a force or a motion after the interaction throughnearby agents in a dense crowd. Moreover, the method is also ableto handle interactions with dynamic obstacles, as opposed to theprevious techniques only considered static obstacles.

The proposed method provides a stable and fast simulation of alarge and dense crowd. Since the method is able to handle dynamicobstacles interacting with the agents and is fast enough for real-time interactive simulation, users can participate in the simulationby moving rigid bodies inside the scene. Fig. 2 shows an examplescenario that consists of several pieces of furniture and 1200 agents.As the user inserts flying pink boxes into the scene, the agents getpushed, collide into each other, and maneuver to avoid falling ob-jects. More details and results are available on the project websitehttp://gamma.cs.unc.edu/CrowdInteractions/.

Figure 2: Agents navigate to avoid office furniture (left). As usersinsert flying pink boxes into the scene, the agents get pushed, col-lide into each other, and avoid falling objects (right) [6].

3 EXPECTED CONTRIBUTIONS AND FUTURE WORK

In summary, I propose following techniques to improve crowd be-havior model.Dynamic Crowd Behaviors: I propose a method to simulate dy-namic patterns and variabilities of individual behaviors based on

psychological theories. The method can handle spatially and tem-porally varying situational factors in a unified framework. The pro-posed method is able to generate several emergent behaviors in dif-ferent scenarios that match real-world observations.Adaptive Motion Model: I propose a method to improve the mo-tion model by learning from real-world data. This method is ableto learn individual characteristics in an online manner, without anyprior knowledge to the scene. The result can be used to predict themotion of the individuals in the scene, which can improve the qual-ity of the interaction between the users and the computer system forhuman-computer interactions, human-robot interactions, computertracking applications, or augmented reality.Physical Interaction models: I propose a method to model newtypes of interactions involving physical contact such as collisionresponse and intentional physical interactions. This method han-dles physical interactions while still preserving the ability of thecrowd to avoid collisions with each other. In addition, agent-objectinteractions and agent-user interactions are available at interactiverates. The method will be useful for games, augmented reality orvirtual reality applications where the user can directly interact withthe virtual agents or the objects in the scene.

The next step of my research for the thesis will focus on maxi-mizing the benefit of the previous approaches in terms of an intu-itive design and greater control of crowds. I am able to simulate alarger variety of crowd behaviors with improved accuracy and re-alism, and now I would like to find a way to quickly design andcontrol the crowd simulation scenarios. This work will be usefulfor the applications like augmented reality, animation, games, vir-tual environment systems, and crowd analysis systems.

REFERENCES

[1] F. Durupinar, N. Pelechano, J. Allbeck, U. Gu anddu andkbay, andN. Badler. How the ocean personality model affects the perception ofcrowds. Computer Graphics and Applications, IEEE, 31(3):22 –31,2011.

[2] S. J. Guy, J. Chhugani, S. Curtis, M. C. Lin, P. Dubey, andD. Manocha. Pledestrians: A least-effort approach to crowd simu-lation. In Symposium on Computer Animation. ACM, 2010.

[3] S. J. Guy, S. Kim, M. C. Lin, and D. Manocha. Simulating heteroge-neous crowd behaviors using personality trait theory. In Symposiumon Computer Animation, pages 43–52. ACM, 2011.

[4] F. Heider. The psychology of interpersonal relations. Lawrence Erl-baum Associates, 1982.

[5] S. Kim, S. Guy, W. Liu, R. Lau, M. Lin, and D. Manocha. Predictingpedestrian trajectories using velocity-space reasoning. In The TenthInternational Workshop on the Algorithmic Foundations of Robotics(WAFR), 2012.

[6] S. Kim, S. J. Guy, and D. Manocha. Velocity-based modeling of phys-ical interactions in multi-agent simulations. In Eurographics/ ACMSIGGRAPH Symposium on Computer Animation, 2013.

[7] S. Kim, S. J. Guy, D. Manocha, and M. C. Lin. Interactive simulationof dynamic crowd behaviors using general adaptation syndrome the-ory. In Symposium on Interactive 3D Graphics, pages 55–62. ACM,2012.

[8] K. H. Lee, M. G. Choi, Q. Hong, and J. Lee. Group behavior fromvideo: a data-driven approach to crowd simulation. In Symposium onComputer Animation, pages 109–118, 2007.

[9] S. Pellegrini, A. Ess, K. Schindler, and L. Van Gool. You’ll never walkalone: Modeling social behavior for multi-target tracking. In Proc. ofthe (IEEE) 12th International Conference on Computer Vision, pages261–268, Sept 2009.

[10] J. Pettre, J. Ondrej, A.-H. Olivier, A. Cretual, and S. Donikian.Experiment-based modeling, simulation and validation of interactionsbetween virtual walkers. In Symposium on Computer Animation, SCA’09, pages 189–198. ACM, 2009.

[11] K. Yamaguchi, A. Berg, L. Ortiz, and T. Berg. Who are you withand where are you going? In Proc. of the 2011 IEEE Conferenceon Computer Vision and Pattern Recognition, pages 1345–1352, june2011.

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