The implementation of autonomous, flocking crowds of background characters in the feature film “Robots” is discussed. The techniques for obstacle avoidance and goal seeking are described. An overview of the implementation of the system as part of the production pipeline for the film is also provided.

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With the increased use of crowd simulation in animation, specification of crowd motion can be very time consuming, requiring a lot of user input. To alleviate this cost, we wish to allow a user to interactively manipulate the many degrees of freedom in a crowd, whilst accounting for the limitation of low-dimensional signals from standard input devices. In this thesis we present two approaches for achieving this: 1) Combining shape deformation methods with a multitouch input device, allowing a user to control the motion of the crowd in dynamic environments, and 2) applying a data-driven approach to learn the mapping between a crowd’s motion and the corresponding user input to enable intuitive control of a crowd. In our first approach, we represent the crowd as a deformable mesh, allowing a user to manipulate it using a multitouch device. The user controls the shape and motion of the crowd by altering the mesh, and the mesh in turn deforms according to the environment. We handle congestion and perturbation by having agents dynamically reassign their goals in the formation using a mass transport solver. Our method allows control of a crowd in a single pass, improving on the time taken by previous, multistage, approaches. We validate our method with a user study, comparing our control algorithm against a common mouse-based controller. We develop a simplified version of motion data patches to model character-environment interactions that are largely ignored in previous crowd research. We design an environment-aware cost metric for the mass transport solver that considers how these interactions affect a character’s ability to track the user’s commands. Experimental results show that our system can produce realistic crowd scenes with minimal, high-level, input signals from the user. In our second approach, we propose that crowd simulation control algorithms inherently impose restrictions on how user input affects the motion of the crowd. To bypass this, we investigate a data-driven approach for creating a direct mapping between low-dimensional user input and the resulting high-dimensional crowd motion. Results show that the crowd motion can be inferred directly from variations in a user’s input signals, providing a user with greater freedom to define the animation.

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Crowds of people are gathering at multiple venues, such as concerts, political rallies, as well as in commercial malls, or just simply walking on the streets. More and more people are flocking to live in urban areas, thus generating a lot of scenarios of crowds. As a consequence, there is an increasing demand for automatic tools that can analyze and predict the behavior of crowds to ensure safety. Crowd motion analysis is a key feature in surveillance and monitoring applications, providing useful hints about potential threats to safety and security in urban and public spaces. It is well known that people gatherings are generally difficult to model, due to the diversity of the agents composing the crowd. Each individual is unique, being driven not only by the destination but also by personality traits and attitude. The domain of crowd analysis has been widely investigated in the literature. However, crowd gatherings have sometimes resulted in dangerous scenarios in recent years, such as stampedes or during dangerous situations. To take a step toward ensuring the safety of crowds, in this work we investigate two main research problems: we try to predict each person future position and we try to understand which are the key factors for simulating crowds. Predicting in advance how a mass of people will fare in a given space would help in ensuring the safety of public gatherings.

This thesis presents the design and evaluation of the location of a cell phone user, to enable more effective performance monitoring. One of the end-uses I propose is in emergency management, by means of a framework that distributes its functionality between establishing data-set characteristics that are relevant to the problem and a visual tool to evaluate resource-scheduling proposals. Manikarnika is a modular framework, which finds translation in a prototype for Reverse 111. The first steps in the process were to establish whether the parameters I hypothesized as useful, indeed were. Using a statistically significant amount of traces, obtained from real calls placed on the network, the utility of the location metric was established. In order to investigate utilizing a second metric of reputation, a benchmark for evaluating ideas from Social Networks research was proposed, in order to move from arbitrary testing to a more systematic environment. This dissertation details the measurement, design and evaluation of an end-to-end and modular framework for Emergency Management, where the functionality is distributed in order to easily incorporate the changing parameters of sources of information, emergency events, resource requirements of these events and identifying callers that might be able to provide better insight into a situation that is essentially very dynamic. The chasm between research proposals for various end-uses and the application of the same to real life is one that I have tried to bridge in my work. By incorporating pieces from core Electrical Engineering measurements and simulation and extending the use of what was originally a tool built for training Emergency Responders to analyze various resource scheduling agents, which take into account a diversity of administrative domains, I lay the ground work for one possible solution, Reverse 111, which proposes the use of proactive crowd-sourcing for emergency response, with easy extensions to commercial location-based applications.

The aim of this thesis is to extend the social identity model of crowd behaviour (Reicher, 1984, 1987, 1996) by exploring the experience of collective participation, with an emphasis upon quality of within-crowd social relations (‘relatedness') and collective emotionality. A multi-method research strategy is employed to study these topics at a variety of crowd events. Studies 1 and 2 use ethnography and retrospective interviews at a student protest and public screenings of football matches to generate hypotheses for subsequent testing. Study 3 then tests these hypotheses by means of questionnaire data collected during Study 2. Using an experimental ‘visualisation' paradigm, Study 4 demonstrates the role of shared identity (and not simply self-categorization) in generating relatedness. Studies 5 and 6 present evidence that groups of high relatedness participants experience identity-relevant stimuli as more emotionally intense than low relatedness groups. Study 7 concludes the empirical work by using questionnaire data collected at political protests to test a model of collective experience based upon the findings of the previous studies. The thesis argues that a perception of shared identity with co-present others can positively transform social relations towards relatedness (connectedness, validation and recognition). Relatedness may then be experienced emotionally, and facilitate the realisation of group goals which may also have emotional consequences. Strength of social identity is also noted as an antecedent to group-based emotion. In this way the analysis offers three ways in which social identity may lead to emotionality of collective experience, contradicting ‘classic' crowd psychology in which crowd emotion was rooted in a loss of identity. Preliminary evidence is also presented suggesting that the experience of collective participation may have a role to play in determining future social identification and participation in co-action.

Street performers have to create and manage their own performance events. This makes street performance an ideal type of situation for studying how an audience is assembled and sustained in practice. This thesis uses detailed video-based ethnographic analysis to investigate these processes in street performances in Covent Garden, London. Drawing on the performance literature, the role of the physical structure of the environment, the arrangement of physical objects within the environment and the physical placement of people are all examined. The argument of the thesis is that these analyses alone are insufficient to explain how an audience is established or sustained. Rather, an audience is an ongoing interactional achievement built up through a structured sequence of interactions between performers, passers-by and audience members. Through these interactions performers get people’s attention, achieve the recognition that what is going on is a performance, build a collective sense of audience membership, establish moral obligations to each other and the performer, and train the audience how to respond. The interactional principles uncovered in this thesis establish the audience as a social group worthy of studying in its own right, and are in support of a multiparty human-human interaction approach to design for crowds and audiences.

The ability to analyse and predict the movement of people in crowded scenarios can be of fundamental importance for tracking across multiple cameras and interaction localisation. In this thesis, we propose a person re-identification method that takes into account the spatial location of cameras using a plan of the locale and the potential paths people can follow in the unobserved areas. These potential paths are generated using two models. In the first, people’s trajectories are constrained to pass through a set of areas of interest (landmarks) in the site. In the second we integrate a goal-driven approach to the Social Force Model (SFM), initially introduced for crowd simulation. SFM models the desire of people to reach specific interest points (goals) in a site, such as exits, shops, seats and meeting points while avoiding walls and barriers. Trajectory propagation creates the possible re-identification candidates, on which association of people across cameras is performed using spatial location of the candidates and appearance features extracted around a person’s head. We validate the proposed method in a challenging scenario from London Gatwick airport and compare it to state-of-the-art person re-identification methods. Moreover, we perform detection and tracking of interacting people in a framework based on SFM that analyses people’s trajectories. The method embeds plausible human behaviours to predict interactions in a crowd by iteratively minimising the error between predictions and measurements. We model people approaching a group and restrict the group formation based on the relative velocity of candidate group members. The detected groups are then tracked by linking their centres of interaction over time using a buffered graph-based tracker. We show how the proposed framework outperforms existing group localisation techniques on three publicly available datasets.

This dissertation is about the early medieval Roman populus and the ways in which early medieval popes used the concept of the populus to legitimize their activities from the start of the Laurentian Schism of 498 to the election of Formosus as pope in 891. These centuries were a time of great change for the Bishop of Rome and it is significant that textual sources link the papacy and the populus for every important event that occurred in early medieval Rome. The populus and the popes had a symbiotic relationship, as crowds participated in all manner of papal ceremonies and the papacy began to use the populus for their own propaganda and to solidify their own power. As the popes made an ever-increasing use of the symbolic meaning of the crowds to extend their authority at the expense of outside powers, those powers also attempted to harness the power of the Roman crowd for their own ends, demonstrating that the populus was a important source of power in early medieval social, religious, and political life.