Weighted margin sparse embedded classifier for brake cylinder detection

This paper proposes a new Weighted Margin Sparse Embedded (WMSE) classifier for brake cylinder detection, which is a big challenge in Trouble of Freight car Detection System (TFDS) of China. The major contributions of this paper are in three folds. (1) The proposed method is a combination of the Sparse Embedded (SE) and the Weighted Margin LearningS (WML) models, which are iteratively performed toward optimal classifier ensemble. The final classifier in cascades takes advantages of VC-dimension minimization and weighted margin learning, which provides a new investigation into the literature of classifier design. (2) Convergence of the WMSE classifier is theoretically proven, which is a desirable characteristic for object detection due to existence of large-scale training datasets in real applications. (3)To evaluate the performance of the proposed method, we establish and distribute the challenging BeiHang Brake Cylinder (BH-BC) Database containing over 2000 annotated brake cylinder images with various appearances and almost indistinguishable backgrounds. Comparative experimental results on the BH-BC database show that our approach can get a much higher detection performance than the state-of-the-art classifiers (Support Vector Machine and Adaboost).