Training Cascade Compact CNN With Region-IoU for Accurate Pedestrian Detection

Abstract

Recently, pedestrian detection has made significant advances benefiting from the region-based convolutional neural networks (R-CNN). However, training R-CNN with a holistic intersection over union (IoU) always brings many flawed positive samples. This paper introduces a strict matching metric, which is beneficial to selecting well-aligned positive samples. Specifically, this matching metric is defined on a set of region-IoUs instead of a holistic IoU, which considers the alignments of different part regions in a whole bounding box simultaneously. A positive sample matches a ground truth only if all its region-IoUs are bigger than a threshold. Secondly, an improved negative example selection strategy using both the classification and localization information is proposed to mine hard negative examples, which can further suppress the false positive detections near the pedestrians. Based on the proposed sample selection strategy, a cascade compact convolutional neural network (CC-CNN) is proposed for accurate pedestrian detection. Each stage of the CC-CNN is constructed with a compact network that only consists of a small number of parameters, thus making the detector suitable to be implemented on onboard embedded systems. Experimental results on two widely used pedestrian datasets demonstrate that the proposed training strategy and the CC-CNN based detector can effectively improve the detection rate and the localization accuracy using fewer parameters.