Refine pedestrian detections by referring to features in different ways

Abstract

Region Proposal Network (RPN) itself which is used for region proposals in Faster R-CNN can be used as a pedestrian detector. Also, RPN even shows better performance than Faster R-CNN for pedestrian detection. However, RPN generates severe false positives such as high score backgrounds and double detections because it does not have downstream classifier. From this observations, we made a network to refine results generated from the RPN. Our Refinement Network refers to the feature maps of the RPN and trains the network to rescore severe false positives. Also, we found that different type of feature referencing method is crucial for improving performance.