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Multispectral pedestrian detection has received extensive attention in recentyears as a promising solution to facilitate robust human target detection foraround-the-clock applications (e.g. security surveillance and autonomousdriving). In this paper, we demonstrate illumination information encoded inmultispectral images can be utilized to significantly boost performance ofpedestrian detection. A novel illumination-aware weighting mechanism is presentto accurately depict illumination condition of a scene. Such illuminationinformation is incorporated into two-stream deep convolutional neural networksto learn multispectral human-related features under different illuminationconditions (daytime and nighttime). Moreover, we utilized illuminationinformation together with multispectral data to generate more accurate semanticsegmentation which are used to boost pedestrian detection accuracy. Putting allof the pieces together, we present a powerful framework for multispectralpedestrian detection based on multi-task learning of illumination-awarepedestrian detection and semantic segmentation. Our proposed method is trainedend-to-end using a well-designed multi-task loss function and outperformsstate-of-the-art approaches on KAIST multispectral pedestrian dataset.

Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection