Generally, crowd datasets can be collected or generated from real or synthetic sources. Real data is generated by using infrastructure-based sensors (such as static cameras or other sensors). The use of simulation tools can significantly reduce the time required to generate scenario-specific crowd datasets, facilitate data-driven research, and next build functional machine learning models. The main goal of this work was to develop an extension of crowd simulation (named CrowdSim2) and prove its usability in the application of people-tracking algorithms. The simulator is developed using the very popular Unity 3D engine with particular emphasis on the aspects of realism in the environment, weather conditions, traffic, and the movement and models of individual agents. Finally, three methods of tracking were used to validate generated dataset: IOU-Tracker, Deep-Sort, and Deep-TAMA.

翻译：通常，人群数据集可以通过真实来源或合成来源收集或生成。真实数据通过使用基础设施传感器（如静态摄像头或其他传感器）生成。使用仿真工具可显著减少生成特定场景人群数据集所需的时间，促进数据驱动研究，并进一步构建功能性机器学习模型。本研究的主要目标是开发一个名为CrowdSim2的人群模拟扩展，并验证其在行人跟踪算法应用中的可用性。该模拟器采用广受欢迎的Unity 3D引擎开发，特别强调环境、天气条件、交通以及个体智能体的运动与模型的逼真性。最后，采用三种跟踪方法（IOU-Tracker、Deep-Sort和Deep-TAMA）对生成的数据集进行了验证。