  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.
