关键词: 基于区域的全卷积网络（R-FCN）, 遮挡, 背景混淆干扰, 二次分类

Abstract: Pedestrian detection is an active topic of research in computer vision. In order to implement the detection of pedestrian in complicated environments, Region-based Fully Convolutional Networks（R-FCN） is proposed to pedestrian detection in this study. Addressed to occlusion, background interference and small objects in pedestrian detection, this paper changes the searching mechanism of R-FCN, and introduces area division for target pedestrian（upper part, lover part of pedestrian） and reinforcement learning strategy of background interference, to strengthen the learning of occlusion and samples similar to the background. On this basis, the second-time classification on the output of the R-FCN is operated. The experimental results indicate that the improvement of R-FCN effectively solves the problem of missing and misclassification from traditional R-FCN classifier under the condition of occlusion, background interference and small objects.

Key words: Region-based Fully Convolutional Networks（R-FCN）, occlusion, background interference, second-time classification