Pedestrian Proposal and Refining Based on the Shared Pixel Differential Feature

We design a pedestrian proposal and refining system tailored for fast pedestrian detection. The pedestrian proposal is based on pixel differential feature (PDF), which is a light weighted feature with a high recall rate. For the pedestrian refining, we propose an aggregated region feature (ARF) to distill the co-existing dominant pixel differential patterns in a local region to reject hard false positives. Albeit discriminative, ARF largely relies on the size of anchored regions and the scale of the PDF, which hinders its performance in real-world applications. Although multi-scale PDF with spatial pyramid somewhat alleviates this problem, it is computationally expensive and thus infeasible in practice. To address this issue, we further propose a directional radius pooling method to extract discriminative information in each orientation of PDF while reducing the feature dimensionality with a more compact size. The pedestrian proposal and refining framework is built on the shared pixel differential feature map which is very computationally efficient. More specifically, a set of pedestrian proposals generated from the single-scale PDF are first obtained in images. Second, multi-scale ARF in spatial pyramid is used to fuse information from different scales and spatial resolutions for anchor regions. Third, the directional radius pooling method is proposed to extract dominant information of each orientation in the anchor regions. The pedestrian proposal and refining are finally integrated for accurate pedestrian detection. The extensive experimental evaluations on five public benchmarks show that our method achieves state-of-the-art results while running at 18 fps for $480\times640$ images.