  Thermal images are mainly used to detect the presence of people at night or
in bad lighting conditions, but perform poorly at daytime. To solve this
problem, most state-of-the-art techniques employ a fusion network that uses
features from paired thermal and color images. Instead, we propose to augment
thermal images with their saliency maps, to serve as an attention mechanism for
the pedestrian detector especially during daytime. We investigate how such an
approach results in improved performance for pedestrian detection using only
thermal images, eliminating the need for paired color images. For our
experiments, we train the Faster R-CNN for pedestrian detection and report the
added effect of saliency maps generated using static and deep methods (PiCA-Net
and R3-Net). Our best performing model results in an absolute reduction of miss
rate by 13.4% and 19.4% over the baseline in day and night images respectively.
We also annotate and release pixel level masks of pedestrians on a subset of
the KAIST Multispectral Pedestrian Detection dataset, which is a first publicly
available dataset for salient pedestrian detection.
