Abstract
We present a novel interactive multi-agent simulation algorithm to model pedestrian movement dynamics. We use statistical techniques to compute the movement patterns and motion dynamics from 2D trajectories extracted from crowd videos. Our formulation extracts the dynamic behavior features of real-world agents and uses them to learn movement characteristics on the fly. The learned behaviors are used to generate plausible trajectories of virtual agents as well as for long-term pedestrian trajectory prediction. Our approach can be integrated with any trajectory extraction method, including manual tracking, sensors, and online tracking methods. We highlight the benefits of our approach on many indoor and outdoor scenarios with noisy, sparsely sampled trajectory in terms of trajectory prediction and data-driven pedestrian simulation.
Highlights
The modeling of pedestrian movement dynamics has received considerable attention in multiple fields, including computer-aided design, urban planning, robotics, and evacuation planning
We demonstrate its applications for many data-driven crowd simulations, where we can add hundreds of virtual pedestrians, generate dense crowds, and change the environment or the situation
Our goal is to develop robust techniques that can account for noise in trajectory datasets and extract high-level characteristics of time-varying pedestrian movement dynamics (TVPMD)
Summary
The modeling of pedestrian movement dynamics has received considerable attention in multiple fields, including computer-aided design, urban planning, robotics, and evacuation planning. In many of these applications, the goal is to generate trajectories and behaviors of virtual pedestrians that are similar to those observed of humans in realworld environments. The most common approaches used to model pedestrian and crowd movement are based on agent-based models that treat individuals as autonomous agents who can perceive the environment to make independent decisions about their behavior or movement. Agent-based methods have been well studied in different fields for decades. Current approaches are unable to simulate the dynamic nature, variety, and subtle aspects of real-world pedestrian motions
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