Google Street View images support the development of vision-based driver assistance systems

For the development of vision-based driver assistance systems, large amounts of data are needed, e.g., for training machine learning approaches, tuning parameters, and comparing different methods. There are basically three possible ways to obtain the required data: using freely available benchmark sets, doing own recordings, or falling back to synthesized sequences. In this paper, we show that Google Street View can be incorporated as a valuable source for image data. Street View is the largest publicly available collection of images recorded from a drivers´ perspective, covering many different countries and scenarios. We describe how to efficiently access the data and present a framework that allows for virtual driving through a network of images. We assess its performance and show its applicability in practice considering traffic sign recognition as an example. The introduced approach supports an efficient collection of image data relevant to training and evaluating machine vision modules. It is easily adaptable and extendible, whereby Street View becomes a valuable tool for developers of vision-based assistance systems.