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We gather the requirements visually impaired road users have concerning a camera-based assistive system in order to develop a concept for transferring algorithms from Advanced Driver Assistance Systems (ADAS) to this domain. We therefore combine procedures from software engineering, especially requirements engineering, with methods from qualitative social sciences, namely expert interviews by means of problem-centered interview methodology. The evaluation of the interviews results in a total of six traffic scenarios, that are of interest for the assistance of visually impaired road users, clustered into three categories: orientation, pedestrian and public transport scenarios. From each scenario, we derive use cases based on computer vision and state which of them are also addressed in ADAS so that we can examine them further in the future to formulate the transfer concept. We present a general literature review concerning solutions from ADAS for the identified overlapping use cases from both fields and take a closer look on how to adapt ADAS Lane Detection algorithms to the assistance of the visually impaired.

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Herein, we research the possibilities of assisting visually impaired pedestrians moving in traffic situations by using camera-based detection of relevant objects in their immediate surroundings. Therefore, we use and adapt algorithms from the field of driver assistance. We present a road background segmentation based on watersheds, whose results are used as input for the presented crosswalk and lane detection algorithms. The crosswalk detection is based on the application of two 1D mean filters and the lane detection on local computations of the EDF (Edge Distribution Function). In our evaluation, the described algorithms achieved good hit rates of 99.87% (road segmentation), 98.64% (crosswalk detection), and 97.89% (lane detection).