Simulation is a powerful tool to easily generate annotated data, and a highly desirable feature, especially in those domains where learning models need large training datasets. Machine learning and deep learning solutions, have proven to be extremely data-hungry and sometimes, the available real-world data are not sufficient to effectively model the given task. Despite the initial skepticism of a portion of the scientific community, the potential of simulation has been largely confirmed in many application areas, and the recent developments in terms of rendering and virtualization engines, have shown a good ability also in representing complex scenes. This includes environmental factors, such as weather conditions and surface reflectance, as well as human-related events, like human actions and behaviors. We present a human crowd simulator, called UniCrowd, and its associated validation pipeline. We show how the simulator can generate annotated data, suitable for computer vision tasks, in particular for detection and segmentation, as well as the related applications, as crowd counting, human pose estimation, trajectory analysis and prediction, and anomaly detection.

翻译：仿真是轻松生成带标注数据的强大工具，尤其在模型训练需要大规模数据集的领域极具价值。机器学习与深度学习方法已被证实极度依赖数据，而在实际应用中，现有真实世界数据往往不足以有效建模特定任务。尽管学术界部分研究者曾持怀疑态度，但仿真技术在众多应用领域的潜力已得到广泛验证，近年来渲染与虚拟化引擎的发展也展现出其模拟复杂场景的能力。这既包括天气条件与表面反射率等环境因素，也涵盖人类行为及动作等与人群相关的事件。本文提出名为UniCrowd的人群仿真器及其配套验证流程，展示该仿真器如何生成适用于计算机视觉任务（特别是目标检测与分割）的带标注数据，以及人群计数、人体姿态估计、轨迹分析与预测、异常检测等相关应用场景。