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The standard pipeline in pedestrian detection is sliding a pedestrian model on an image feature pyramid to detect pedestrians of different scales. In this pipeline, feature pyramid construction is time consuming and becomes the bottleneck for fast detection. Recently, a method called multiresolution filtered channels (MRFC) was proposed which only used single scale feature maps to achieve fast detection. However, as MRFC use gridwise sampling in the feature extraction process, the receptive field correspondence in different scales is weak. This shortcoming limits its accuracy. In this paper, we proposed a method which also uses single scale feature maps. The main difference between MRFC and our method lies in feature extraction. As opposed to using gridwise sampling, we use scale-aware pooling, which makes a better receptive field correspondence. Experiment on Caltech dataset shows our detector achieves fast detecting speed at the same time with high accuracy.
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