41-Issue 8

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https://diglib.eg.org/handle/10.2312/2633208

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Browsing 41-Issue 8 by Subject "Applied computing"

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    Cognitive Model of Agent Exploration with Vision and Signage Understanding
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Johnson, Colin; Haworth, Brandon; Dominik L. Michels; Soeren Pirk
    Signage systems play an essential role in ensuring safe, stress-free, and efficient navigation for the occupants of indoor spaces. Crowd simulations with sufficiently realistic virtual humans provide a convenient and cost-effective approach to evaluating and optimizing signage systems. In this work, we develop an agent model which makes use of image processing on parametric saliency maps to visually identify signage and distractions in the agent's field of view. Information from identified signs is incorporated into a grid-based representation of wayfinding familiarity, which is used to guide informed exploration of the agent's environment using a modified A* algorithm. In areas with low wayfinding familiarity, the agent follows a random exploration behaviour based on sampling a grid of previously observed locations for heuristic values based on space syntax isovist measures. The resulting agent design is evaluated in a variety of test environments and found to be able to reliably navigate towards a goal location using a combination of signage and random exploration.
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    A Fusion Crowd Simulation Method: Integrating Data with Dynamics, Personality with Common
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Mao, Tianlu; Wang, Ji; Meng, Ruoyu; Yan, Qinyuan; Liu, Shaohua; Wang, Zhaoqi; Dominik L. Michels; Soeren Pirk
    This paper proposes a novel crowd simulation method which integrates not only modelling ideas but also advantages from both data-driven methods and crowd dynamics methods. To seamlessly integrate these two different modelling ideas, first, a fusion crowd motion model is developed. In this model the motion of crowd are driven dynamically by different forces. Part of the forces are modeled under a universal interaction mechanism, which describe the common parts of crowd dynamics. Others are modeled by examples from real data, which describe the personality parts of the agent motion. Second, a construction method for example dataset is proposed to support the fusion model. In the dataset, crowd trajectories captured in the real world are decomposed and re-described under the structure of the fusion model. Thus, personality parts hidden in the real data could be locked and extracted, making the data understandable and migratable for our fusion model. A comprehensive crowd motion generation workflow using the fusion model and example dataset is also proposed. Quantitative and qualitative experiments and user studies are conducted. Results show that the proposed fusion crowd simulation method can generate crowd motion with the great motion fidelity, which not only match the macro characteristics of real data, but also has lots of micro personality showing the diversity of crowd motion.