10,151 research outputs found
Path planning for complex 3D multilevel environments
The continuous development of graphics hardware is
contributing to the creation of 3D virtual worlds with
high level of detail, from models of large urban areas, to
complete infrastructures, such as residential buildings,
stadiums, industrial settings or archaeological sites, to
name just a few. Adding virtual humans or avatars adds
an extra touch to the visualization providing an enhanced
perception of the spaces, namely adding a sense of scale,
and enabling simulations of crowds. Path planning for
crowds in a meaningful way is still an open research
field, particularly when it involves an unknown polygonal
3D world. Extracting the potential paths for navigation in
a non automated fashion is no longer a feasible option
due to the dimension and complexity of the virtual
environments available nowadays. This implies that we
must be able to automatically extract information from
the geometry of the unknown virtual world to define
potential paths, determine accessibilities, and prepare a
navigation structure for real time path planning and path
finding. A new image based method is proposed that
deals with arbitrarily a priori unknown complex virtual
worlds, namely those consisting of multilevel passages
(e.g. over and below a bridge). The algorithm is capable
of extracting all the information required for the actual
navigation of avatars, creating a hierarchical data
structure to help both high level path planning and low
level path finding decisions. The algorithm is image
based, hence it is tessellation independent, i.e. the
algorithm does not use the underlying polygonal structure
of the 3D world. Therefore, the number of polygons as
well as the topology, do not affect the performance
Path planning for complex 3D multilevel environments
The continuous development of graphics hardware is
contributing to the creation of 3D virtual worlds with
high level of detail, from models of large urban areas, to
complete infrastructures, such as residential buildings,
stadiums, industrial settings or archaeological sites, to
name just a few. Adding virtual humans or avatars adds
an extra touch to the visualization providing an enhanced
perception of the spaces, namely adding a sense of scale,
and enabling simulations of crowds. Path planning for
crowds in a meaningful way is still an open research
field, particularly when it involves an unknown polygonal
3D world. Extracting the potential paths for navigation in
a non automated fashion is no longer a feasible option
due to the dimension and complexity of the virtual
environments available nowadays. This implies that we
must be able to automatically extract information from
the geometry of the unknown virtual world to define
potential paths, determine accessibilities, and prepare a
navigation structure for real time path planning and path
finding. A new image based method is proposed that
deals with arbitrarily a priori unknown complex virtual
worlds, namely those consisting of multilevel passages
(e.g. over and below a bridge). The algorithm is capable
of extracting all the information required for the actual
navigation of avatars, creating a hierarchical data
structure to help both high level path planning and low
level path finding decisions. The algorithm is image
based, hence it is tessellation independent, i.e. the
algorithm does not rely on the underlying polygonal
structure of the 3D world. Therefore, the number of
polygons does not have a significant impact on the
performance, and the topology has no weight on the
results.Fundação para a Ciência e a Tecnologi
Overview of crowd simulation in computer graphics
High-powered technology use computer graphics in education, entertainment, games, simulation, and virtual heritage applications has led it to become an important area of research. In simulation, according to Tecchia et al. (2002), it is important to create an interactive, complex, and realistic virtual world so that the user can have an immersive experience during navigation through the world. As the size and complexity of the environments in the virtual world increased, it becomes more necessary to populate them with peoples, and this is the reason why rendering the crowd in real-time is very crucial. Generally, crowd simulation consists of three important areas. They are realism of behavioral (Thompson and Marchant 1995), high-quality visualization (Dobbyn et al. 2005) and convergence of both areas. Realism of behavioral is mainly used for simple 2D visualizations because most of the attentions are concentrated on simulating the behaviors of the group. High quality visualization is regularly used for movie productions and computer games. It gives intention on producing more convincing visual rather than realism of behaviors. The convergences of both areas are mainly used for application like training systems. In order to make the training system more effective, the element of valid replication of the behaviors and high-quality visualization is added
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
Populating 3D Cities: a True Challenge
In this paper, we describe how we can model crowds in real-time using dynamic meshes, static meshes andimpostors. Techniques to introduce variety in crowds including colors, shapes, textures, individualanimation, individualized path-planning, simple and complex accessories are explained. We also present ahybrid architecture to handle the path planning of thousands of pedestrians in real time, while ensuringdynamic collision avoidance. Several behavioral aspects are presented as gaze control, group behaviour, aswell as the specific technique of crowd patches
Hierarchical path-finding for Navigation Meshes (HNA*)
Path-finding can become an important bottleneck as both the size of the virtual environments and the number of agents navigating them increase. It is important to develop techniques that can be efficiently applied to any environment independently of its abstract representation. In this paper we present a hierarchical NavMesh representation to speed up path-finding. Hierarchical path-finding (HPA*) has been successfully applied to regular grids, but there is a need to extend the benefits of this method to polygonal navigation meshes. As opposed to regular grids, navigation meshes offer representations with higher accuracy regarding the underlying geometry, while containing a smaller number of cells. Therefore, we present a bottom-up method to create a hierarchical representation based on a multilevel k-way partitioning algorithm (MLkP), annotated with sub-paths that can be accessed online by our Hierarchical NavMesh Path-finding algorithm (HNA*). The algorithm benefits from searching in graphs with a much smaller number of cells, thus performing up to 7.7 times faster than traditional A¿ over the initial NavMesh. We present results of HNA* over a variety of scenarios and discuss the benefits of the algorithm together with areas for improvement.Peer ReviewedPostprint (author's final draft
Placing large group relations into pedestrian dynamics: psychological crowds in counterflow
Understanding influences on pedestrian movement is important to accurately simulate crowd behaviour, yet little research has explored the psychological factors that influence interactions between large groups in counterflow scenarios. Research from social psychology has demonstrated that social identities can influence the micro-level pedestrian movement of a psychological crowd, yet this has not been extended to explore behaviour when two large psychological groups are co-present. This study investigates how the presence of large groups with different social identities can affect pedestrian behaviour when walking in counterflow. Participants (N = 54) were divided into two groups and primed to have identities as either ‘team A’ or ‘team B’. The trajectories of all participants were tracked to compare the movement of team A when walking alone to when walking in counterflow with team B, based on their i) speed of movement and distance walked, and ii) proximity between participants. In comparison to walking alone, the presence of another group influenced team A to collectively self-organise to reduce their speed and distance walked in order to walk closely together with ingroup members. We discuss the importance of incorporating social identities into pedestrian group dynamics for empirically validated simulations of counterflow scenarios
Crowd modeling and simulation technologies
As a collective and highly dynamic social group, the human crowd is a fascinating phenomenon that has been frequently studied by experts from various areas. Recently, computer-based modeling and simulation technologies have emerged to support investigation of the dynamics of crowds, such as a crowd's behaviors under normal and emergent situations. This article assesses the major existing technologies for crowd modeling and simulation. We first propose a two-dimensional categorization mechanism to classify existing work depending on the
size
of crowds and the
time-scale
of the crowd phenomena of interest. Four evaluation criteria have also been introduced to evaluate existing crowd simulation systems from the point of view of both a modeler and an end-user.
We have discussed some influential existing work in crowd modeling and simulation regarding their major features, performance as well as the technologies used in this work. We have also discussed some open problems in the area. This article will provide the researchers with useful information and insights on the state of the art of the technologies in crowd modeling and simulation as well as future research directions.</jats:p
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