2,993 research outputs found

    Multiagent System Applied to the Modeling and Simulation of Pedestrian Traffic in Counterflow

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    An agent-based model to simulate a pedestrian crowd in a corridor is presented. Pedestrian crowd models are valuable tools to gain insight into the behavior of human crowds in both, everyday and crisis situations. The main contribution of this work is the definition of a pedestrian crowd model by applying ideas from the field of the kinetic theory of living systems on the one hand, and ideas from the field of computational agents on the other hand. Such combination supported a quantitative characterization of the performance of our agents, a neglected issue in agent-based models, through well-known kinetic parameters. Fundamental diagrams of flow and activity are presented for both, groups of homogeneous pedestrians, and groups of heterogeneous pedestrians in terms of their willingness to reach their goals.Agent-Based Modeling, Pedestrian Crowd, Activity Measurement

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

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    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

    Overview of crowd simulation in computer graphics

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    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

    Socially Constrained Structural Learning for Groups Detection in Crowd

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    Modern crowd theories agree that collective behavior is the result of the underlying interactions among small groups of individuals. In this work, we propose a novel algorithm for detecting social groups in crowds by means of a Correlation Clustering procedure on people trajectories. The affinity between crowd members is learned through an online formulation of the Structural SVM framework and a set of specifically designed features characterizing both their physical and social identity, inspired by Proxemic theory, Granger causality, DTW and Heat-maps. To adhere to sociological observations, we introduce a loss function (G-MITRE) able to deal with the complexity of evaluating group detection performances. We show our algorithm achieves state-of-the-art results when relying on both ground truth trajectories and tracklets previously extracted by available detector/tracker systems
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