Context-aware lane marking detection on urban roads

Automatic lane marking detection plays an important role in intelligent transportation systems. We present an effective lane marking detection technique that utilizes the context-aware information of lane marking on the urban roads. The proposed technique consists of two innovations. First, the context-aware color, texture and shape features which characterise both lane markings and their road context are designed to represent the lane markings on the road surface. Second, a hard negative mining technique is developed based on the Maximum Stable Extreme Region (MSER) detector and adaboost training. Experiments on a real world dataset demonstrate the superior performance of the proposed approach.