As autonomous vehicles become an every-day reality, high-accuracy pedestrian detection is of paramount practical importance. Pedestrian detection is a highly researched topic with mature methods, but most datasets focus on common scenes of people engaged in typical walking poses on sidewalks. But performance is most crucial for dangerous scenarios, such as children playing in the street or people using bicycles/skateboards in unexpected ways. Such "in-the-tail" data is notoriously hard to observe, making both training and testing difficult. To analyze this problem, we have collected a novel annotated dataset of dangerous scenarios called the Precarious Pedestrian dataset. Even given a dedicated collection effort, it is relatively small by contemporary standards (around 1000 images). To allow for large-scale data-driven learning, we explore the use of synthetic data generated by a game engine. A significant challenge is selected the right "priors" or parameters for synthesis: we would like realistic data with poses and object configurations that mimic true Precarious Pedestrians. Inspired by Generative Adversarial Networks (GANs), we generate a massive amount of synthetic data and train a discriminative classifier to select a realistic subset, which we deem the Adversarial Imposters. We demonstrate that this simple pipeline allows one to synthesize realistic training data by making use of rendering/animation engines within a GAN framework. Interestingly, we also demonstrate that such data can be used to rank algorithms, suggesting that Adversarial Imposters can also be used for "in-the-tail" validation at test-time, a notoriously difficult challenge for real-world deployment.
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