In this work, we consider the problem of pedestrian detection in natural
scenes. Intuitively, instances of pedestrians with different spatial scales may
exhibit dramatically different features. Thus, large variance in instance
scales, which results in undesirable large intra-category variance in features,
may severely hurt the performance of modern object instance detection methods.
We argue that this issue can be substantially alleviated by the
divide-and-conquer philosophy. Taking pedestrian detection as an example, we
illustrate how we can leverage this philosophy to develop a Scale-Aware Fast
R-CNN (SAF R-CNN) framework. The model introduces multiple built-in
sub-networks which detect pedestrians with scales from disjoint ranges. Outputs
from all the sub-networks are then adaptively combined to generate the final
detection results that are shown to be robust to large variance in instance
scales, via a gate function defined over the sizes of object proposals.
Extensive evaluations on several challenging pedestrian detection datasets well
demonstrate the effectiveness of the proposed SAF R-CNN. Particularly, our
method achieves state-of-the-art performance on Caltech, INRIA, and ETH, and
obtains competitive results on KITTI.