Learning Cross-Modal Deep Representations for Robust Pedestrian Detection

@article{Xu2017LearningCD,
  title={Learning Cross-Modal Deep Representations for Robust Pedestrian Detection},
  author={Dan Xu and Wanli Ouyang and Elisa Ricci and Xiaogang Wang and Nicu Sebe},
  journal={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017},
  pages={4236-4244}
}
This paper presents a novel method for detecting pedestrians under adverse illumination conditions. Our approach relies on a novel cross-modality learning framework and it is based on two main phases. First, given a multimodal dataset, a deep convolutional network is employed to learn a non-linear mapping, modeling the relations between RGB and thermal data. Then, the learned feature representations are transferred to a second deep network, which receives as input an RGB image and outputs the… CONTINUE READING
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