Image-based classification of driving scenes by Hierarchical Principal Component Classification (HPCC)
作者： Robert Kastner , Frank Schneider , Thomas Michalke , Jannik Fritsch , Christian Goerick
关键词: Context (language use) 、 Usability 、 Contextual image classification 、 Statistical classification 、 Image segmentation 、 Data mining 、 Engineering 、 Lane departure warning system 、 Advanced driver assistance systems 、 Feature extraction
摘要: State-of-the-art advanced driver assistance systems (ADAS) typically focus on single tasks and therefore, have functionalities with clearly defined application areas. Although said ADAS functions (e.g. lane departure warning) show good performance, they lack general usability, as e.g. different modes of operation for highways country roads. This paper presents a real-time capable approach, which classifies the driving scene by using newly developed Hierarchical Principal Component Classification (HPCC). Based that, an gets information about current context is able to activate modes. Exemplarily, algorithm was trained three categories (highways, roads, inner city), but can be applied any number type categories. Evaluation results 9000 images reliability approach mark it crucial step towards more sophisticated high level applications.
tu-darmstadt.de
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