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Pedestrian detection methods have been significantly improved with thedevelopment of deep convolutional neural networks. Nevertheless, robustlydetecting pedestrians with a large variant on sizes and with occlusions remainsa challenging problem. In this paper, we propose a gated multi-layerconvolutional feature extraction method which can adaptively generatediscriminative features for candidate pedestrian regions. The proposed gatedfeature extraction framework consists of squeeze units, gate units and aconcatenation layer which perform feature dimension squeezing, feature elementsmanipulation and convolutional features combination from multiple CNN layers,respectively. We proposed two different gate models which can manipulate theregional feature maps in a channel-wise selection manner and a spatial-wiseselection manner, respectively. Experiments on the challenging CityPersonsdataset demonstrate the effectiveness of the proposed method, especially ondetecting those small-size and occluded pedestrians.

Gated Multi-layer Convolutional Feature Extraction Network for Robust Pedestrian Detection