  Virtual Human Simulation has been widely used for different purposes, such as
comfort or accessibility analysis. In this paper, we investigate the
possibility of using this type of technique to extend the training datasets of
pedestrians to be used with machine learning techniques. Our main goal is to
verify if Computer Graphics (CG) images of virtual humans with a simplistic
rendering can be efficient in order to augment datasets used for training
machine learning methods. In fact, from a machine learning point of view, there
is a need to collect and label large datasets for ground truth, which sometimes
demands manual annotation. In addition, find out images and videos with real
people and also provide ground truth of people detection and counting is not
trivial. If CG images, which can have a ground truth automatically generated,
can also be used as training in machine learning techniques for pedestrian
detection and counting, it can certainly facilitate and optimize the whole
process of event detection. In particular, we propose to parametrize virtual
humans using a data-driven approach. Results demonstrated that using the
extended datasets with CG images outperforms the results when compared to only
real images sequences.
