Stereo Vision and Color Image Evaluation for Combined Scene Segmentation and Detection of Traffic Participants

Modern driver assistance systems such as collision avoidance or intersection assistance need reliable information on the current environment. Extracting such information from camera-based systems is a complex and challenging task for inner city traffic scenarios. This paper presents an approach to combined scene segmentation and object detection using stereo and color information. The extracted features must be sufficient for a road geometry estimation in an urban environment. Therefore, a probabilistic formulation of segmentation and object classification is mandatory. This project in particular investigates features that are relevant for inner city road geometry such as house fronts or principle axes of cars.