A combined grammar for object detection and pose estimation

Consider that the limitation of part-based models on the description of object categories, we propose a discriminative grammar model. The model, which has powerful description ability and extensibility, can represent general objects and deal with common recognition tasks. We define two instantiations of the grammar model for object detection and pose estimation and then discuss the differences and similarities between them. Viewed from application background and current research methods, there is great complementarity in object detection and pose estimation. This paper further introduces a novel grammar that is constructed by combining two single-task grammars using a set of discriminative symbols. There are two characteristics for the combined grammar. First, it supports joint detection and pose estimation. Second, it can improve the detection performance of both tasks. For learning grammar parameters with weak supervision we utilize a structural SVM with latent variables. We compare the combined grammar with part-based models in single-task scenario and multiple-task scenario. The evaluated results demonstrate that the proposed grammar outperforms the state-of-the-art detection models and pose estimation models.