Research on Low-Resolution Pedestrian Detection Algorithms based on R-CNN with Targeted Pooling and Proposal

Abstract

We present an effective low-resolution pedestrian detection using targeted pooling and Region Proposal Network (RPN) in the Faster R-CNN. Our method firstly rearranges the anchor from the RPN exploiting an optimal hyper-parameter setting called "Elaborate Setup". Secondly, it refines the granularity in the pooling operation from the ROI pooling layer. The experimental results demonstrate that the proposed RPN together with fine-grained pooling, which we call LRPD-R-CNN is able to achieve high average precision and robust performance on the VOC 2007 dataset. This method has great potential in commercial values and wide application prospect in the field of computer vision, security and intelligent city.