Simultaneous prototype selection and outlier isolation for traffic sign recognition: A collaborative sparse optimization method

Video-based traffic sign recognition is one of the most important task for unmanned autonomous vehicle. However, there always exists unavoidable outliers in the practical scenario. Therefore, robust prototype extraction from the noisy sample set is highly expected to help traffic sign recognition in video sequence. In this paper, we propose a novel approach for simultaneous prototype extraction and outlier isolation through collaborative sparse learning. The new model accounts for not only the reconstruction capability and the sparsity, but also the robustness. To solve the optimization problem, we adopt the Alternating Directional Method of Multiplier (ADMM) technology to design an iterative algorithm. Finally, the effectiveness of the approach is demonstrated by experiments on GTSRB dataset.