Generative Adversarial Networks for Parallel Vision

Video image dataset is playing an essential role in design and evaluation of traffic vision methods. However, there is a longstanding difficulty that manually collecting and annotating large-scale diversified dataset from real scenes is time-consuming and prone to error. In 2016, we proposed the parallel vision methodology to tackle the issues of conventional vision computing approach in data collection, model learning and evaluation. We built the ParallelEye dataset with virtual reality and the scene-specific virtual pedestrian dataset with augmented reality. In the dataset compiling process, the graphics rendering engine was used to render the artificial scenes and generate virtual images. However, the fidelity of virtual images is not satisfactory due to limitation of rendering engine, so that there is a distribution gap between virtual data and real data. In our opinion, Generative Adversarial Networks (GANs) can generate more realistic images for parallel vision research. We introduce some GANs and explain their utility in parallel vision.