following that of Faster R-CNNs [@RenEtAl:nips15]. Training this module involves estimating the weight parameters of the pedestrian proposal network as depicted in the green box of Fig. \[fig\_2\]. The objective function $L_{\mathcal{D}} \left(\mathcal{W} \right)$ is thus a weight parameterized neural network function defined over our training examples $\mathcal{D}$, where $\mathcal{W}$ stands for the weight parameters. As it contains multi-layer feature representations as input, let $l^m \left( y_i, \hat{y}_i | \mathcal{W} \right)$ denote the loss function of the $i$-th training example at the $m$-th layer of the Resnet feature representation, and without loss of generality, denote by $\mathcal{M}=\{3,4,5\}$ the aforementioned Resnet layers considered in our paper. The objective function is then formulated as $$\begin{aligned}
\label{eq1}
L_{\mathcal{D}}(\mathcal{W})=\sum\limits_{m \in \mathcal{M}} \sum\limits_{i\in{\mathcal{D}^m}} \alpha_m l^m( y_i, \hat{y}_i|\mathcal{W}),\end{aligned}$$ where $\alpha_m$ is the weight of current loss $l^m( y_i, \hat{y}_i|\mathcal{W})$ that can be computed by the softmax function $\alpha_m = \frac{e^{\hat{\alpha}_m}} {\sum\limits_{m'=1}^M e^{\hat{\alpha}_{m'}}}$, with $\hat{\alpha}_m = \frac{1} {(1+e^{- \frac{h-\bar{h}_m}{\gamma_m} })}$. $\bar{h}_m$ denoting the average height of the pedestrians from the $m$-th layer in the training set. For example, the respective $\bar{h}_m$ values in Caltech benchmark are 48, 96, and 156. ${\gamma}_m$ is a scaling factor, which is empirically set to 5, 20, and 10, respectively for the 