  This paper presents a novel method for detecting pedestrians under adverse
illumination conditions. Our approach relies on a novel cross-modality learning
framework and it is based on two main phases. First, given a multimodal
dataset, a deep convolutional network is employed to learn a non-linear
mapping, modeling the relations between RGB and thermal data. Then, the learned
feature representations are transferred to a second deep network, which
receives as input an RGB image and outputs the detection results. In this way,
features which are both discriminative and robust to bad illumination
conditions are learned. Importantly, at test time, only the second pipeline is
considered and no thermal data are required. Our extensive evaluation
demonstrates that the proposed approach outperforms the state-of- the-art on
the challenging KAIST multispectral pedestrian dataset and it is competitive
with previous methods on the popular Caltech dataset.
