DVRNet: Decoupled Visible Region Network for Pedestrian Detection

Lei Shi, Charles Livermore, Ioannis A. Kakadiaris

Abstract: Pedestrian detection remains a challenging task due to the problems caused by occlusion variance. Visible-body bounding boxes are typically used as an extra supervision signal to improve the performance of pedestrian detection to predict the full-body. However, visible-body assisted approaches produce a large number of false positives, which result from a lack of adequate and discriminative full-body contextual information. In this paper, we propose a new network, dubbed DVRNet, based on the representative visible-body assisted pedestrian detector named Bi-box. Specifically, we extend Bi-box by adding three modules named the attention-based feature interleaver module (AFIM), the binary mask learning module (BMLM), and the head-aware feature enhancement module (HFEM), which play important roles in employing features learned by the visible-body and the head supervision signals to enrich high discriminative contextual information of the full-body and enhance the power of feature representation. Experimental results indicate that the DVRNet achieves promising results on the CityPersons and the CrowdHuman datasets.