Single-Stage Detector with Semantic Attention for Occluded Pedestrian Detection

Abstract

In this paper, we propose a pedestrian detection method with semantic attention based on the single-stage detector architecture (i.e., RetinaNet) for occluded pedestrian detection, denoted as PDSA. PDSA contains a semantic segmentation component and a detector component. Specifically, the first component uses visible bounding boxes for semantic segmentation, aiming to obtain an attention map for pedestrians and the inter-class (non-pedestrian) occlusion. The second component utilizes the single-stage detector to locate the pedestrian from the features obtained previously. The single-stage detector adopts over-sampling of possible object locations, which is faster than two-stage detectors that train classifier to identify candidate object locations. In particular, we introduce the repulsion loss to deal with the intra-class occlusion. Extensive experiments conducted on the public CityPersons dataset demonstrate the effectiveness of PDSA for occluded pedestrian detection, which outperforms the state-of-the-art approaches.