number of negative examples are first uniformly sampled. The training set is then bootstrapped by ranking and sampling the negative examples according to their objectness scores to obtain hard negative examples. As potentially there are far more negative candidates, a balancing constant $\gamma\geq 1$ is used such that the negative set is constrained by $\left| \mathcal{D}_-^m \right|=\gamma \left| \mathcal{D}_+^m \right|$.

Finally, by considering contributions of both positive and negative examples, the classification loss term now becomes a weighted cross-entropy:

$$\begin{aligned}
\label{eq_l_cls}
l_{\mathrm{cls}}=\frac{1}{1+\gamma}\frac{1}{\left| \mathcal{D}_+^m \right|} \sum\limits_{i\in{\mathcal{D}_+^m}}-\log \hat{p_i} + \frac{\gamma}{1+\gamma}\frac{1}{\left| \mathcal{D}_-^m \right|} \sum\limits_{j\in{\mathcal{D}_-^m}} \log \hat{p_j}.\end{aligned}$$

![A schematic illustration of our active detection module in the process of producing a series of transformation action $\left( \ldots, a_t, a_{t+1}, \ldots \right)$, when working with a possible pedestrian instance $\vec{x}$ of an input image. See text for details.[]{data-label="fig_5"}](TIP_Fig5.pdf){width="3.0in"}

![The set of actions considered in our localization policy.[]{data-label="fig_6"}](TIP_Fig6.pdf){width="0.69\linewidth"}

Our Active Detector Model
-------------------------

In conjunction with the initial proposals described in the previous section, an active detection model is introduced for improving pedestrian bbox predictions by performing a series of coordinate transformation actions. It allows an agent to adaptively choose the feature maps of proper resolutions catering for current pedestrian distances from our multi-layer feature representation. 