details due to small sized receptive fields from lower-layers. This is further illustrated in Fig. \[fig\_4\], where far-scale pedestrians are most comfortably picked up at lower layers, and near-scale ones are largely detected at upper layers. Meanwhile, it appears to be difficult to identify far-scale ones at upper layers, and vice versa. In practice, three layers namely ResNet-50-C3, ResNet-50-C4, and ResNet-50-C5, are chosen as the working scale space in this paper.

As displayed in Fig. \[fig\_2\], an input image is passed through the ResNet layers to form multi-layer feature representations, where bbox proposals of different scales are also generated. The region of interest (RoI) pooling layer of [@YanChoLin:cvpr16] is then utilized to pool the feature maps of each pedestrian proposal into a fixed-length feature vector, which is fed into a fully connected layer. At last, each pedestrian proposal $\hat{\vec{y}}$ ends up with two outputs, one accounts for classification score $\hat{p}$, and the other one predicts the bbox position $\hat{\vec{b}}$.

Up to now it should be clear that this module serves the purpose of providing a multi-layer feature representation of the input image. We then aim to produce predictions on both initial bbox locations (pedestrian proposals) and the associated objectness score, 