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This paper presents a novel method for detecting pedestrians under adverseillumination conditions. Our approach relies on a novel cross-modality learningframework and it is based on two main phases. First, given a multimodaldataset, a deep convolutional network is employed to learn a non-linearmapping, modeling the relations between RGB and thermal data. Then, the learnedfeature representations are transferred to a second deep network, whichreceives as input an RGB image and outputs the detection results. In this way,features which are both discriminative and robust to bad illuminationconditions are learned. Importantly, at test time, only the second pipeline isconsidered and no thermal data are required. Our extensive evaluationdemonstrates that the proposed approach outperforms the state-of- the-art onthe challenging KAIST multispectral pedestrian dataset and it is competitivewith previous methods on the popular Caltech dataset.

Learning Cross-Modal Deep Representations for Robust Pedestrian Detection