portion that results in obscure appearance and blurred boundaries, as displayed in Fig. \[fig\_1\](d). It is in general difficult to distinguish them from the background clutters. Second, for a pedestrian instance of interest, visual features are effective only at a proper scale where optimal response is obtained. The issue is more pronounced in complex scenes containing pedestrian instances of diverse scales.

![In pedestrian detection, a typical input image usually contains multiple pedestrian instances over different scales. (a) An input image from the Caltech benchmark [@DolEtAl:cvpr09]. (b) The scale distribution of pedestrian heights from the same Caltech dataset. One can observe that far-scale instances in fact dominate the distribution. (c) and (d) display exemplar visual appearance between near- and far-scale instances, as well as the corresponding neuronal feature representations from the proper layers, which are noticeably different.[]{data-label="fig_1"}](TIP_Fig1.pdf){width="0.79\linewidth"}

Motivated by these observations, we consider in this paper a dedicated approach with a more balanced competency in detecting both near- and far-scale pedestrian instances. It possesses the following three major contributions. First, a novel active detection approach is proposed to take as input multi-layer neuronal feature representations as well as initial pedestrian proposals of the input still image, execute sequences of coordinate transformation 