layer.

A high-level overview of our approach is displayed in Fig. \[fig\_2\], which consists of two main stages: An input image will first pass through an initialization stage to make ready the multi-layer feature representations and the initial pedestrian proposals, they are then fed into an active detection stage where a dedicated localization policy is engaged to produce the final bbox predictions $\left\{ \hat{\vec{y}}_i \right\}$ by executing sequences of coordinate transformation actions.

![Visualization of the pedestrian proposal activations across multiple neural representation layers. See text for more details.[]{data-label="fig_3"}](TIP_Fig3_Feature.pdf){width="0.89\linewidth"}

![A visual elucidation on the effect range of our resultant initial proposals at each layer of the feature representation hierarchy.[]{data-label="fig_4"}](TIP_Fig4_Proposals.pdf){width="0.98\linewidth"}

Multi-layer Representation and Initial Pedestrian Proposals {#sec:section 3}
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It has been shown in Fig. \[fig\_1\] that given multiple pedestrian instances of different scales being presented in a typical image, their best responses usually occur at distinct feature representation layers. This is revealed in more details at Fig. \[fig\_2\], where three intermediate convolutional layers of ResNet [@HeEtAl:cvpr16], i.e. ResNet-50-C3, ResNet-50-C4 and ResNet-50-C5, are employed as our multi-layer feature representations. Moreover, Fig. \[fig\_2\] provides more detailed information over the first five ResNet layers (i.e. ResNet-50-C1 to ResNet-50-C5). Here the class activation maps of 