Crowd Modeling

Work on levels of detail for human simulation has occurred mainly on a geometrical level, either by reducing the numbers of polygons representing a virtual human, or replacing them with a two-dimensional imposter. Approaches that reduce the complexity of motions generated have also been proposed. In this paper, we describe ongoing development of a framework for Adaptive Level Of Detail for Human Animation (ALOHA), which incorporates levels of detail for not only geometry and motion, but also includes a complexity gradient for natural behaviour, both conversational and social.

This paper investigates a range of challenges faced in the design of a serious game aimed at teaching history in situ, through the use of an immersive, open virtual environment. In the context of this paper, such an environment is described as an exploratory, expansive virtual world within which a user may interact in a non-linear and situated fashion with the virtual characters that populate it. The main contribution of this paper consists of the introduction of the Levels of Interaction (LoI) framework, designed to assist in the creation of multiple forms of interaction between a user-driven avatar and synthetic characters. The LoI approach addresses the necessity for balancing computational efficiency with the need to provide believable and interactive virtual characters, allowing varying degrees of visual, interactive and behavioural fidelity. The Roma Nova project demonstrates a first implementation of the concept, showing in practice how the LoI are likely to foster more natural interactions between the player and the non-playing characters.

This paper investigates a range of challenges faced in the design of a serious game aimed at teaching history in situ, through the use of an immersive, open virtual environment. In the context of this paper, such an environment is described as an exploratory, expansive virtual world within which a user may interact in a non-linear and situated fashion with

Large-scale crowd simulation and visualization is crucial for the next generation of interactive virtual environments. Current authoring techniques produce good results but are laborious, and demand valuable graphics memory and computational resources beyond the reach of consumer-level hardware. In this paper, we propose a technique for generating animatable characters for crowds that reduces memory requirements. The first step consists in reducing, segmenting and labeling a data set of virtual characters into simpler body parts; labeling information is then manually generated and used to correctly match different body parts in order to generate new characters. The second step comprises a method to embed the rig and skinning information into the texture space shared among the new characters. Additional methods using color, skin features, pattern, fat, wrinkle and textile fold maps are used to add more variety. Animation sequences are stored in auxiliary textures. These can be transferred between different characters, as well as versions of the same characters with different level of detail; such animations can be modified, and otherwise reused, increasing variety yet reducing memory requirements. We will demonstrate that our technique has four advantages: first, memory requirements are reduced by 91% when compared to traditional libraries; second, it can also generate previously nonexistent characters from the original data set; third, embedding the rig and skinning into texture space allows painless animation transfer between different characters and fourth, between different levels of detail of the same characters.

This volume presents the latest theory and research on crowd events and crowd behaviour from across a range of social sciences – including psychology, sociology, law, and communication studies. Whether describing the language of the crowd in protest events, measuring the ability of the crowd to empower its participants, or analysing the role of professional organizations involved in crowd safety and public order, what unites these different contributions is a commitment to a social scientific level of analysis. The crowd is often depicted as a source of irrationality and danger – in the form of riots and mass emergencies. By placing crowd events back in their social context – their ongoing historical and proximal relationships with other groups and social structures – the contributions to this edited volume restore meaning to the analysis of crowd behaviour. Together, the studies described in this collection demonstrate the potential of crowd research to enhance the positive experience of crowd participants and to improve design, planning, and management around crowd events.

This book was originally published as a special issue of Contemporary Social Science.

This paper describes a viewpoint invariant learning-based method for counting people in crowds from a single camera. Our method takes into account feature normalization to deal with perspective projection and different camera orientation. The training features include edge orientation and blob size histograms resulted from edge detection and background subtraction. A density map that measures the relative size of individuals and a global scale measuring camera orientation are estimated and used for feature ...

The SAPERE Project http://www. sapere-project. eu/SAPERE (Self-aware Pervasive Service Ecosystems) is an EU STREP project under the FP7 FET Proactive Initiative: Self-Awareness in Autonomic Systems (AWARENESS) The objective of SAPERE is the development of a highly-innovative theoretical and practical framework for the decentralized deployment and execution of self-aware and adaptive services for future and emerging pervasive network scenarios

In the domain of real-time urban vistas, a modern vision of a vast populace inhabiting a sprawling metropolis is replacing that of the ghost-towns of past. Working towards this ideal, we describe the methodology we are using to model dynamic crowd scenarios and an exemplar based on it. Video corpus is consulted for informing the modelling process and perception based evaluations are conducted on the resultant scenarios with the goal of improving the visual plausibility of the crowd, rather than correctness of simulation. Using this methodology, the exemplar focuses on modelling small groups of pedestrians. While it is generally assumed that the addition of groups can improve the plausibility of crowd scenarios, little is known about how humans perceive the results. We shed light on these issues, demonstrating the practical application of the methodology in a real-time crowd animation system.