  Pedestrian detection methods have been significantly improved with the
development of deep convolutional neural networks. Nevertheless, robustly
detecting pedestrians with a large variant on sizes and with occlusions remains
a challenging problem. 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 a
concatenation layer which perform feature dimension squeezing, feature elements
manipulation and convolutional features combination from multiple CNN layers,
respectively. We proposed two different gate models which 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 those small-size and occluded pedestrians.
