VR Toolkit for Identifying Group Characteristics

Visualising crowds is a key pedestrian dynamics topic, with significant research efforts aiming to improve the current state-of-the-art. Sophisticated visualisation methods are a standard for modern commercial models, and can improve crowd management techniques and sociological theory development. These models often define standard metrics, including density and speed. However, modern visualisation techniques typically use desktop screens. This can limit the capability of a user to investigate and identify key features, especially in real time scenarios such as control centres. Virtual reality (VR) provides the opportunity to represent scenarios in a fully immersive environment, granting the user the ability to quickly assess situations. Furthermore, these visualisations are often limited to the simulation model that has generated the dataset, rather than being source-agnostic. In this paper we implement an immersive, interactive toolkit for crowd behaviour analysis. This toolkit was built specifically for use within VR environments and was developed in conjunction with commercial users and researchers. It allows the user to identify locations of interest, as well as individual agents, showing characteristics such as group density, individual (Voronoi) density and speed. Furthermore, it was used as a data-extraction tool, building individual fundamental diagrams for all scenario agents, and predicting group status as a function of local agent geometry. Finally, this paper presents an evaluation of the toolkit made by crowd behaviour experts

LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning

We present a novel procedural framework to generate an arbitrary number of labeled crowd videos (LCrowdV). The resulting crowd video datasets are used to design accurate algorithms or training models for crowded scene understanding. Our overall approach is composed of two components: a procedural simulation framework for generating crowd movements and behaviors, and a procedural rendering framework to generate different videos or images. Each video or image is automatically labeled based on the environment, number of pedestrians, density, behavior, flow, lighting conditions, viewpoint, noise, etc. Furthermore, we can increase the realism by combining synthetically-generated behaviors with real-world background videos. We demonstrate the benefits of LCrowdV over prior lableled crowd datasets by improving the accuracy of pedestrian detection and crowd behavior classification algorithms. LCrowdV would be released on the WWW

An information theory based behavioral model for agent-based crowd simulations

Crowds must be simulated believable in terms of their appearance and behavior to improve a virtual environment’s realism. Due to the complex nature of human behavior, realistic behavior of agents in crowd simulations is still a challenging problem. In this paper, we propose a novel behavioral model which builds analytical maps to control agents’ behavior adaptively with agent-crowd interaction formulations. We introduce information theoretical concepts to construct analytical maps automatically. Our model can be integrated into crowd simulators and enhance their behavioral complexity. We made comparative analyses of the presented behavior model with measured crowd data and two agent-based crowd simulators

Group emotion modelling and the use of middleware for virtual crowds in video-games

In this paper we discuss the use of crowd simulation in video-games to augment their realism. Using previous works on emotion modelling and virtual crowds we define a game world in an urban context. To achieve that, we explore a biologically inspired human emotion model, investigate the formation of groups in crowds, and examine the use of physics middleware for crowds. Furthermore, we assess the realism and computational performance of the proposed approach. Our system runs at interactive frame-rate and can generate large crowds which demonstrate complex behaviour