  Data augmentation has proven its usefulness to improve model generalization
and performance. While it is commonly applied in computer vision application
when it comes to multi-view systems, it is rarely used. Indeed geometric data
augmentation can break the alignment among views. This is problematic since
multi-view data tend to be scarce and it is expensive to annotate. In this work
we propose to solve this issue by introducing a new multi-view data
augmentation pipeline that preserves alignment among views. Additionally to
traditional augmentation of the input image we also propose a second level of
augmentation applied directly at the scene level. When combined with our simple
multi-view detection model, our two-level augmentation pipeline outperforms all
existing baselines by a significant margin on the two main multi-view
multi-person detection datasets WILDTRACK and MultiviewX.
