  Across a majority of pedestrian detection datasets, it is typically assumed
that pedestrians will be standing upright with respect to the image coordinate
system. This assumption, however, is not always valid for many vision-equipped
mobile platforms such as mobile phones, UAVs or construction vehicles on rugged
terrain. In these situations, the motion of the camera can cause images of
pedestrians to be captured at extreme angles. This can lead to very poor
pedestrian detection performance when using standard pedestrian detectors. To
address this issue, we propose a Rotational Rectification Network (R2N) that
can be inserted into any CNN-based pedestrian (or object) detector to adapt it
to significant changes in camera rotation. The rotational rectification network
uses a 2D rotation estimation module that passes rotational information to a
spatial transformer network to undistort image features. To enable robust
rotation estimation, we propose a Global Polar Pooling (GP-Pooling) operator to
capture rotational shifts in convolutional features. Through our experiments,
we show how our rotational rectification network can be used to improve the
performance of the state-of-the-art pedestrian detector under heavy image
rotation by up to 45%
