Camera based detection and classification of soft shoulders, curbs and guardrails

This paper describes the extension of an existing commercial lane detection system for marked roads by the detection and classification of soft shoulders, curbs and guardrails. Available sensors are a front directed gray scale camera and the CAN-bus data. To accomplish our goals we use two complementing methods. The first method is meant for localizing the border using a texture based area classification. We use Local Binary Patterns for their quality as well as their ability for being realized on an embedded system and a neural network as classifier. The second method is a kind of structure from motion for identification of raised structures like guardrails. The algorithm tracks Harris features using the Lucas and Kanade tracker and extracts 3D information out of it. The whole system works in real time and achieves an availability of about 80% with a detection rate of 94% on a test database, which includes roads under almost all weather conditions throughout a number of countries.