representation layers, which is enforced if the size of the object of interest is less than 4$\times$4. This way the contextual information is incorporated especially for far-scale pedestrian instances. The approach is followed by a resize operation where larger objects (i.e. with sizes exceeding 4$\times$4) are rescaled with bicubic interpolation to 4$\times$4. Thus the feature vector dimensions of these representation layers become $4096=4\times4\times256$, 8192, and 16384, respectively. Now, at any time $t \in \left\{1,\ldots, T\right\}$, the following neural net functions are engaged: First, a succinct description of this instance distilled from $m$-th layer of its ResNet feature representation is provided by a fully connected (fc) layer and a follow-up vector valued ReLU activation function, as $$\begin{aligned}
\label{eq_ot}
\vec{o}_t := f_{\theta_{\vec{o}}}\left( \vec{b}_{t-1}, \vec{x} \right) = \max \left( \theta_{\vec{o}}^{(m)} \phi_t \left(\vec{x}\right), 0 \right),\end{aligned}$$ where $m = m(t) \in \mathcal{M}$ denotes the particular layer of interest as considered in $\phi_t \left(\vec{x}\right)$ at time $t$. Second, an internal information state of $\vec{s}_t$ at time $t$ is maintained that summarizes the information extracted from the history of past observations $\vec{s}_{t-1}$ and the current environment: $$\begin{aligned}
\label{eq_st}
\vec{s}_t := f_{\theta_{\vec{s}}}\left( \vec{o}_t, \vec{s}_{t-1} \right) = \tanh \left( \theta_{\vec{s},1} \vec{o}_t + \theta_{\vec{s},1} \vec{s}_{t-1} \right),\end{aligned}$$ with $\theta_{\vec{s}} = \left\{ 