Real-time pedestrian detection with deep supervision in the wild

Pedestrian detection is a challenging research task, and it is widely applied in automatic driving and intelligent surveillance fields. Although many approaches based on deep learning have shown effectiveness for detecting pedestrian, these approaches are difficult to achieve a good trade-off between real time and accuracy. In this paper, a new pedestrian detection algorithm is proposed to address the above problem, and then, a new pedestrian dataset is introduced to evaluate detection performance in our experiment. Our model contains region generation module and region prediction module, and our model allows for parallel processing of two modules for speed. The feature pyramid strategy is adopted in generation module to make full use of features, and deconvolution layers are used to obtain more high-level feature contextual. The deep supervision idea is introduced to prediction module to guide the detection results toward ground truth. Eventually, the proposed method is evaluated on three different datasets (INRIA, ETH and Caltech) and compared with other existing state-of-the-arts, and the experimental results present the competitive accuracy and real time of the proposed method.