Gated Multi-Layer Convolutional Feature Extraction Network for Robust Pedestrian Detection
Tianrui Liu, Junjie Huang, Tianhong Dai, Guangyu Ren, Tania Stathaki
Abstract
Pedestrian detection methods have been significantly improved with the development of deep convolutional neural networks. Nevertheless, it remains a challenging problem how to robustly detect pedestrians of varied sizes and with occlusions. In this paper, we propose a gated multi-layer convolutional feature extraction method which can adaptively generate discriminative features for candidate pedestrian regions. The proposed gated feature extraction framework consists of squeeze units, gate units and concatenation layers which perform feature dimension squeezing, feature manipulation and features combination from multiple CNN layers, respectively. We proposed two different gate models that can manipulate the regional feature maps in a channel-wise selection manner and a spatial-wise selection manner, respectively. Experiments on the challenging CityPersons dataset demonstrate the effectiveness of the proposed method, especially on detecting small-size and occluded pedestrians.