which could be obtained by for example applying a sliding window. At any time, if the intersection-over-union (IoU) of the current sliding window (anchor) and a closest ground-truth pedestrian bbox is above a relatively high threshold (say 0.5), then its label $\vec{y}$ is assigned as $p=1$ and $\vec{b}$ being the ground-truth bbox. Otherwise, if the IoU is below a relatively low threshold (say 0.3) with any ground-truth pedestrian bboxes, then its label becomes $p=0$ and $\vec{b}$ being zero-valued. We further denote a predicted output of $\vec{x}$ as $\hat{\vec{y}}=\left(\hat{p}, \hat{\vec{b}}\right)$, with $\hat{p}$ being the objectness score, i.e. the probability of being a pedestrian bbox. The training set is composed by a set of (non-)pedestrian examples $\mathcal{D} = \left\{ \vec{x}_i, \vec{y}_i \right\}_{i=1}^D$, which could be obtained by running the sliding window over multiple representation layers of the annotated pedestrian training images. To explicitly account for the fact that these pedestrian instances are at different scales, denote as $M$ the number of neural representation layers (here $M$=3), and the training set is partitioned to $\mathcal{D}= \mathcal{D}^1 \cup \mathcal{D}^2 \cup \ldots \cup \mathcal{D}^M$ and $\mathcal{D}^m \cap \mathcal{D}^n = \varnothing$ $\forall m, n$, with each disjoint subset $\mathcal{D}^m$ containing specifically the training examples at current 