  Data scarcity has become one of the main obstacles to developing supervised
models based on Artificial Intelligence in Computer Vision. Indeed, Deep
Learning-based models systematically struggle when applied in new scenarios
never seen during training and may not be adequately tested in non-ordinary yet
crucial real-world situations. This paper presents and publicly releases
CrowdSim2, a new synthetic collection of images suitable for people and vehicle
detection gathered from a simulator based on the Unity graphical engine. It
consists of thousands of images gathered from various synthetic scenarios
resembling the real world, where we varied some factors of interest, such as
the weather conditions and the number of objects in the scenes. The labels are
automatically collected and consist of bounding boxes that precisely localize
objects belonging to the two object classes, leaving out humans from the
annotation pipeline. We exploited this new benchmark as a testing ground for
some state-of-the-art detectors, showing that our simulated scenarios can be a
valuable tool for measuring their performances in a controlled environment.
