Fast and Robust Object Detection Using Visual Subcategories

Object classes generally contain large intra-class variation, which poses a challenge to object detection schemes. In this work, we study visual subcategorization as a means of capturing appearance variation. First, training data is clustered using color and gradient features. Second, the clustering is used to learn an ensemble of models that capture visual variation due to varying orientation, truncation, and occlusion degree. Fast object detection is achieved with integral image features and pixel lookup features. The framework is studied in the context of vehicle detection on the challenging KITTI dataset.