26,198 research outputs found
Quickest Paths in Simulations of Pedestrians
This contribution proposes a method to make agents in a microscopic
simulation of pedestrian traffic walk approximately along a path of estimated
minimal remaining travel time to their destination. Usually models of
pedestrian dynamics are (implicitly) built on the assumption that pedestrians
walk along the shortest path. Model elements formulated to make pedestrians
locally avoid collisions and intrusion into personal space do not produce
motion on quickest paths. Therefore a special model element is needed, if one
wants to model and simulate pedestrians for whom travel time matters most (e.g.
travelers in a station hall who are late for a train). Here such a model
element is proposed, discussed and used within the Social Force Model.Comment: revised version submitte
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
Feeling crowded yet?: Crowd simulations for VR
With advances in virtual reality technology and its multiple applications, the need for believable, immersive virtual environments is increasing. Even though current computer graphics methods allow us to develop highly realistic virtual worlds, the main element failing to enhance presence is autonomous groups of human inhabitants. A great
number of crowd simulation techniques have emerged in the last decade, but critical details in the crowd's movements and appearance do not meet the standards necessary to convince VR participants that they are present in a real crowd. In this paper, we review recent advances in the creation of immersive virtual crowds and discuss areas that require further work to turn these simulations into more fully immersive and believable experiences.Peer ReviewedPostprint (author's final draft
A survey of real-time crowd rendering
In this survey we review, classify and compare existing approaches for real-time crowd rendering. We first overview character animation techniques, as they are highly tied to crowd rendering performance, and then we analyze the state of the art in crowd rendering. We discuss different representations for level-of-detail (LoD) rendering of animated characters, including polygon-based, point-based, and image-based techniques, and review different criteria for runtime LoD selection. Besides LoD approaches, we review classic acceleration schemes, such as frustum culling and occlusion culling, and describe how they can be adapted to handle crowds of animated characters. We also discuss specific acceleration techniques for crowd rendering, such as primitive pseudo-instancing, palette skinning, and dynamic key-pose caching, which benefit from current graphics hardware. We also address other factors affecting performance and realism of crowds such as lighting, shadowing, clothing and variability. Finally we provide an exhaustive comparison of the most relevant approaches in the field.Peer ReviewedPostprint (author's final draft
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