  Multispectral images of color-thermal pairs have shown more effective than a
single color channel for pedestrian detection, especially under challenging
illumination conditions. However, there is still a lack of studies on how to
fuse the two modalities effectively. In this paper, we deeply compare six
different convolutional network fusion architectures and analyse their
adaptations, enabling a vanilla architecture to obtain detection performances
comparable to the state-of-the-art results. Further, we discover that
pedestrian detection confidences from color or thermal images are correlated
with illumination conditions. With this in mind, we propose an
Illumination-aware Faster R-CNN (IAF RCNN). Specifically, an Illumination-aware
Network is introduced to give an illumination measure of the input image. Then
we adaptively merge color and thermal sub-networks via a gate function defined
over the illumination value. The experimental results on KAIST Multispectral
Pedestrian Benchmark validate the effectiveness of the proposed IAF R-CNN.
