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.

翻译：机动车辆中的人类物体识别在以视觉为基础的自动车辆驱动系统中发挥着重要作用,而行人和道路或街道上的车辆等物体是保护无人驾驶车辆的首要目标,一项挑战是难以探测在野外条件下移动的物体,而光照和图像质量则可能大不相同。在这项工作中,为了应对这一挑战,我们利用带有单一射击探测器(SSD)的深革命基因反反转网络(DCGANs)来处理野生情况。在我们的工作中,GAN接受了低质量图像的培训,以应对智能城市野生条件带来的挑战,同时,SD级联被用作GAN的物体探测器。我们在野外条件下用出租车司机视频在伦敦街头日间和夜间测试了我们的方法,而车辆录像的测试表明,在野生条件下,这一战略可以大大提高探测率。