Data-driven Crowd Modeling Techniques: A Survey

Abstract

Data-driven crowd modeling has now become a popular and effective approach for generating realistic crowd simulation and has been applied to a range of applications, such as anomaly detection and game design. In the past decades, a number of data-driven crowd modeling techniques have been proposed, providing many options for people to generate virtual crowd simulation. This article provides a comprehensive survey of these state-of-the-art data-driven modeling techniques. We first describe the commonly used datasets for crowd modeling. Then, we categorize and discuss the state-of-the-art data-driven crowd modeling methods. After that, data-driven crowd model validation techniques are discussed. Finally, six promising future research topics of data-driven crowd modeling are discussed.

Data-driven Interactive Crowd Management Systems for Metaverse Scenarios

“…In this section, we will highlight key challenges in identifying, describing, and analyzing a crowd in a city. Some of the challenges and opportunities are illustrated in Figure 4 and are described as follows [26]:…”

A method for generating procedural seamless textures

“…As shown in Figure 5 below, assuming that B1 and B2 are two texture blocks to be synthesized, the overlapping regions of B1 and B2 are B1(O) and B2(O), E is the minimum cumulative difference from the bottom edge to each point (i,j) on the overlapping region, and e is the pixel error at each point corresponding to the overlapping region, the calculation formula is as follows (1).…”

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