  In-vehicle human object identification plays an important role in
vision-based automated vehicle driving systems while objects such as
pedestrians and vehicles on roads or streets are the primary targets to protect
from driverless vehicles. A challenge is the difficulty to detect objects in
moving under the wild conditions, while illumination and image quality could
drastically vary. In this work, to address this challenge, we exploit Deep
Convolutional Generative Adversarial Networks (DCGANs) with Single Shot
Detector (SSD) to handle with the wild conditions. In our work, a GAN was
trained with low-quality images to handle with the challenges arising from the
wild conditions in smart cities, while a cascaded SSD is employed as the object
detector to perform with the GAN. We used tested our approach under wild
conditions using taxi driver videos on London street in both daylight and night
times, and the tests from in-vehicle videos demonstrate that this strategy can
drastically achieve a better detection rate under the wild conditions.
