  The low resolution of objects of interest in aerial images makes pedestrian
detection and action detection extremely challenging tasks. Furthermore, using
deep convolutional neural networks to process large images can be demanding in
terms of computational requirements. In order to alleviate these challenges, we
propose a two-step, yes and no question answering framework to find specific
individuals doing one or multiple specific actions in aerial images. First, a
deep object detector, Single Shot Multibox Detector (SSD), is used to generate
object proposals from small aerial images. Second, another deep network, is
used to learn a latent common sub-space which associates the high resolution
aerial imagery and the pedestrian action labels that are provided by the
human-based sources
