  In this work, we examine the feasibility of applying Deep Convolutional
Generative Adversarial Networks (DCGANs) with Single Shot Detector (SSD) as
data-processing technique to handle with the challenge of pedestrian detection
in the wild. Specifically, we attempted to use in-fill completion (where a
portion of the image is masked) to generate random transformations of images
with portions missing to expand existing labelled datasets. In our work, GAN
has been trained intensively on low resolution images, in order to neutralize
the challenges of the pedestrian detection in the wild, and considered humans,
and few other classes for detection in smart cities. The object detector
experiment performed by training GAN model along with SSD provided a
substantial improvement in the results. This approach presents a very
interesting overview in the current state of art on GAN networks for object
detection. We used Canadian Institute for Advanced Research (CIFAR), Caltech,
KITTI data set for training and testing the network under different resolutions
and the experimental results with comparison been showedbetween DCGAN cascaded
with SSD and SSD itself.
