How to balance the speed and the quality is always a challenging issue in pedestrian detection. In this paper, we introduce the Learning model Using Privileged Information (LUPI), which can accelerate the convergence rate of learning and effectively improve the quality without sacrificing the speed. In more detail, we give the clear definition of the privileged information, which is only available at the training stage but is never available for the testing set, for the pedestrian detection problem and show how much the privileged information helps the detector to improve the quality. All experimental results show the robustness and effectiveness of the proposed method, at the same time show that the privileged information offers a significant improvement.
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