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As a core step of obstacle avoidance and path planning, dynamic obstacle detection is critical for autonomous driving. This study aimed to propose a dynamic obstacle detection method based on U–V disparity and residual optical flow for autonomous driving. First, a drivable area of an unmanned vehicle was detected using U–V disparity images. Then, obstacles in the drivable area were detected using U–V disparity images and the geometric relationship between obstacle size and its disparity. Finally, the motion likelihood of each obstacle was estimated by compensating the camera ego-motion. The innovation of the proposed method was that the searching range of the moving obstacles was greatly narrowed by detecting the obstacles in the drivable area, which greatly improved not only the moving obstacle detection efficiency but also the detection accuracy. Datasets from the KITTI benchmark and our self-acquired campus scene data were chosen as testing samples. The experimental results showed that our method could achieve high detection precision, low missed detection rate and less time consumption. [ABSTRACT FROM AUTHOR]

Face recognition technology has been widely used in the field of artificial intelligence. The technology needs to be carried out normally under the appropriate light, however, there is not ideal light, even poor-lighted for the face recognition device, and with the head in deflect angle. The poor-lighted under various head poses will influence the face recognition significantly. To address the issue, we present a novel and practical architecture based on deep fully convolutional neural network and generative adversarial networks for illumination normalization of facial images. The proposed method is termed as illumination normalization generative adversarial network. Compared with previous methods based on deep learning, our approach does not require identity and illumination label as input. We preserve identities of faces by an elaborately-designed generator together with content loss. Moreover, the framework of our scheme is simpler than previous methods based on deep learning. It can address the illumination of frontal and non-frontal face. In order to fairly evaluate the proposed method against state-of-the-art models, the peak signal to noise ratio is employed to estimate the performance of illumination normalization algorithm. Experimental results show that the proposed method achieves favorable normalization results against previous models under various head poses and illumination challenges. [ABSTRACT FROM AUTHOR]