Scale-Aware Hierarchical Detection Network for Pedestrian Detection
Xiaowei Zhang, Shuai Cao, Chenglizhao Chen
Abstract
Several or even dozens of times spatial scale variation is one of the major bottleneck for pedestrian detection. Although the Region-based Convolutional Neural Network (R-CNN) families have shown promising results for object detection, they are still limited to detect pedestrians with large scale variations due to the fixed receptive field sizes on a single convolutional output layer. In contrast to previous methods that simply combined pedestrian predictions on feature maps with different resolution, we propose a scale-aware hierarchical detection network for pedestrian detection under large scale variations. First, we introduce a cross-scale features aggregation module to accomplish feature augmentation for pedestrian representation through merging the lateral connection, the top-down path and bottom-up path. Specifically, the cross-scale features aggregation module can adaptively fuse hierarchical features to enhance feature pyramid representation for robust semantic and accurate localization. Further, we design a scale-aware hierarchical detection network to effectively integrate multiscale pedestrian detection into a unified framework by adaptively perceiving the augmented feature level for special-scale pedestrian detection. Experimentally, the proposed scale-aware hierarchical detection network forms a more robust and discriminative model for pedestrian instances with different scales on widely-used ETH and Caltech benchmarks. In particular, compared with the state-of-the-art method FasterRCNN+ATT, the log-average miss rate of pedestrian detection is reduced by 11.98% for medium scale pedestrians (between 30-80 pixels in height), and 14.12% for whole scale pedestrians (above 20 pixels in height) on Caltech benchmark.