  Pedestrian detection through Computer Vision is a building block for a
multitude of applications. Recently, there was an increasing interest in
Convolutional Neural Network-based architectures for the execution of such a
task. One of these supervised networks' critical goals is to generalize the
knowledge learned during the training phase to new scenarios with different
characteristics. A suitably labeled dataset is essential to achieve this
purpose. The main problem is that manually annotating a dataset usually
requires a lot of human effort, and it is costly. To this end, we introduce
ViPeD (Virtual Pedestrian Dataset), a new synthetically generated set of images
collected with the highly photo-realistic graphical engine of the video game
GTA V - Grand Theft Auto V, where annotations are automatically acquired.
However, when training solely on the synthetic dataset, the model experiences a
Synthetic2Real Domain Shift leading to a performance drop when applied to
real-world images. To mitigate this gap, we propose two different Domain
Adaptation techniques suitable for the pedestrian detection task, but possibly
applicable to general object detection. Experiments show that the network
trained with ViPeD can generalize over unseen real-world scenarios better than
the detector trained over real-world data, exploiting the variety of our
synthetic dataset. Furthermore, we demonstrate that with our Domain Adaptation
techniques, we can reduce the Synthetic2Real Domain Shift, making closer the
two domains and obtaining a performance improvement when testing the network
over the real-world images. The code, the models, and the dataset are made
freely available at https://ciampluca.github.io/viped/
