Multi-Grained Deep Feature Learning for Pedestrian Detection

Abstract

In this paper, we address the challenging problem of detecting pedestrians who are heavily occluded or far from camera. Unlike most existing pedestrian detection methods which only use coarse-resolution feature maps with fixed receptive field, our approach exploits multi-grained deep features to make the detector more robust to visible parts of occluded pedestrians and small-size targets. Specifically, we jointly train a scale-aware network and a human parsing network in a semi-supervised manner with only bounding box annotation. We carefully design the scale-aware network to predict pedestrians of particular scales using most appropriate feature maps, by matching their receptive field with the target sizes. The human parsing network generates a fine-grained attentional map which helps guide the detector to focus on the visible parts of occluded pedestrians and small-size instances. Both networks are computed in parallel and form an unified single stage pedestrian detector, which assures a great trade-off between accuracy and speed. Experiments on two challenging benchmarks, Caltech and KITTI, demonstrate the effectiveness of our proposed approach, which in addition, executes 2× faster than competitive methods.