关键词: 卷积神经网络（CNN）, 行人检测, 选择性搜索, Edge Boxes, 特征提取

Abstract: Aiming at the problems that the percentage of pedestrians in some natural scenes is small（hereinafter referred to as small target）, the extracted features are easily lost, and the detection accuracy is low, a pedestrian detection method based on candidate regions and Parallel Convolutional Neural Network（PCNN） is proposed. First, for the candidate region extraction section, selective search is improved to make it more suitable for pedestrians in this category of candidate region extraction. Then, Edge Boxes are used to filter a large number of pre-candidate regions extracted by selective search. Finally, a small number of high-quality candidate regions are obtained. When using Convolutional Neural Network（CNN） to extract features, deeper convolutional neural networks can extract richer and more abstract high-level features, but at the same time, the small objects can easily cause feature loss, adding shallow convolutional neural network to build a parallel convolutional neural network to extract deep and shallow feature outputs. Finally, the proposed method is applied to pedestrian detection. The experimental results show that the proposed method can improve the detection accuracy of small target.

Key words: Convolutional Neural Network（CNN）, pedestrian detection, selective search, Edge Boxes, feature extraction