  A major bottleneck of pedestrian detection lies on the sharp performance
deterioration in the presence of small-size pedestrians that are relatively far
from the camera. Motivated by the observation that pedestrians of disparate
spatial scales exhibit distinct visual appearances, we propose in this paper an
active pedestrian detector that explicitly operates over multiple-layer
neuronal representations of the input still image. More specifically,
convolutional neural nets such as ResNet and faster R-CNNs are exploited to
provide a rich and discriminative hierarchy of feature representations as well
as initial pedestrian proposals. Here each pedestrian observation of distinct
size could be best characterized in terms of the ResNet feature representation
at a certain layer of the hierarchy; Meanwhile, initial pedestrian proposals
are attained by faster R-CNNs techniques, i.e. region proposal network and
follow-up region of interesting pooling layer employed right after the specific
ResNet convolutional layer of interest, to produce joint predictions on the
bounding-box proposals' locations and categories (i.e. pedestrian or not). This
is engaged as input to our active detector where for each initial pedestrian
proposal, a sequence of coordinate transformation actions is carried out to
determine its proper x-y 2D location and layer of feature representation, or
eventually terminated as being background. Empirically our approach is
demonstrated to produce overall lower detection errors on widely-used
benchmarks, and it works particularly well with far-scale pedestrians. For
example, compared with 60.51% log-average miss rate of the state-of-the-art
MS-CNN for far-scale pedestrians (those below 80 pixels in bounding-box height)
of the Caltech benchmark, the miss rate of our approach is 41.85%, with a
notable reduction of 18.68%.
