Pedestrian crossing is one of the most typical behavior which conflicts with
natural driving behavior of vehicles. Consequently, pedestrian crossing
prediction is one of the primary task that influences the vehicle planning for
safe driving. However, current methods that rely on the practically collected
data in real driving scenes cannot depict and cover all kinds of scene
condition in real traffic world. To this end, we formulate a deep virtual to
real distillation framework by introducing the synthetic data that can be
generated conveniently, and borrow the abundant information of pedestrian
movement in synthetic videos for the pedestrian crossing prediction in real
data with a simple and lightweight implementation. In order to verify this
framework, we construct a benchmark with 4667 virtual videos owning about 745k
frames (called Virtual-PedCross-4667), and evaluate the proposed method on two
challenging datasets collected in real driving situations, i.e., JAAD and PIE
datasets. State-of-the-art performance of this framework is demonstrated by
exhaustive experiment analysis. The dataset and code can be downloaded from the
website http://www.lotvs.net/code_data/.

本文提出了一种通过引入合成数据来构建深度虚拟到现实蒸馏框架，以实现对真实数据中行人过马路的预测，该框架能够从合成视频中获取大量行人运动的信息，在真实交通世界的预测任务中表现出极高的性能表现。