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Thermal images are mainly used to detect the presence of people at night orin bad lighting conditions, but perform poorly at daytime. To solve thisproblem, most state-of-the-art techniques employ a fusion network that usesfeatures from paired thermal and color images. Instead, we propose to augmentthermal images with their saliency maps, to serve as an attention mechanism forthe pedestrian detector especially during daytime. We investigate how such anapproach results in improved performance for pedestrian detection using onlythermal images, eliminating the need for paired color images. For ourexperiments, we train the Faster R-CNN for pedestrian detection and report theadded effect of saliency maps generated using static and deep methods (PiCA-Netand R3-Net). Our best performing model results in an absolute reduction of missrate 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 ofthe KAIST Multispectral Pedestrian Detection dataset, which is a first publiclyavailable dataset for salient pedestrian detection.

Pedestrian Detection in Thermal Images using Saliency Maps