one latest effort,MS-CNN [@CaiEtAl:eccv16] for example, it has been reported that empirically their detector is capable of achieving 3.30% log-average miss rate for near-scale pedestrians in Caltech Pedestrian Benchmark [@DolEtAl:cvpr09], the error rate however increases to 60.51% for medium- and far-scale pedestrians. Similar observations have also been expressed in other works [@HoiChoDai:eccv12; @ZhaEtAl:cvpr16]. It inspires us to consider a simpler grouping of pedestrian instances into two scales: near-scale vs. far-scale, which corresponds to near vs. medium & far in the Caltech benchmark. The focus of this paper is to achieve a more balanced detection performance for both near- and far-scale pedestrian instances.

Our Approach {#sec:section 3}
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We start by laying down some necessary notations. A bounding-box (bbox) coordinate can be identified by $\vec{b}=(b^x,b^y,b^w,b^h) \in \mathbb{R}^4$ for its top-left corner $(b^x,b^y)$, width, and height. At the same time, let $p=p_{\vec{b}} \in \{0, 1\}$ be the probability of whether the bbox is a pedestrian bbox. An input pedestrian instance is denoted as $\vec{x}$, also referred to as an anchor, and its label is $\vec{y}=\left(p, \vec{b}\right)$. Here $\vec{x}$ corresponds to all the information (such as contextual information, specific feature layer representation) in the input image that is related to the pedestrian instance, 