Component-Based License Plate Detection Using Conditional Random Field Model

Abstract

This paper presents a novel algorithm for license plate detection in complex scenes, particularly for the all-day traffic surveillance environment. Unlike low-level feature-based methods, our work is motivated by component-based models for object detection. The detection process is divided into three steps, namely, decomposition, modeling, and inference. First, observing that one license plate is decomposed into several constituent characters, the maximally stable extremal region detector is used to extract candidate characters in images. Then, conditional random field (CRF) models are constructed on the candidate characters in neighborhoods. This way, the spatial and visual relationships among the characters is integrated in CRF in the form of probability distribution. Finally, the exact bounding boxes of license plates are estimated through the belief propagation inference on CRF. Both visual and structural features of license plates are fully exploited during detection. Hence, our approach can adapt to various environmental factors, such as cluttered background and illumination variation. A series of experiments are conducted on images that are collected from the actual road surveillance environment. The experimental results show the outstanding detection performance of the proposed method comparing with traditional algorithms.