  A real-time Deep Learning based method for Pedestrian Detection (PD) is
applied to the Human-Aware robot navigation problem. The pedestrian detector
combines the Aggregate Channel Features (ACF) detector with a deep
Convolutional Neural Network (CNN) in order to obtain fast and accurate
performance. Our solution is firstly evaluated using a set of real images taken
from onboard and offboard cameras and, then, it is validated in a typical robot
navigation environment with pedestrians (two distinct experiments are
conducted). The results on both tests show that our pedestrian detector is
robust and fast enough to be used on robot navigation applications.
