Within intelligent transportation systems, fast and robust license plate localization (LPL) in complex scenes is still a challenging task. Real-world scenes introduce complexities such as variation in license plate size and orientation, uneven illumination, background clutter, and nonplate objects. These complexities lead to poor performance using traditional LPL features, such as color, edge, and texture. Recently, state-of-the-art performance in LPL has been achieved by applying the scale invariant feature transform (SIFT) descriptor to LPL for visual matching. However, for applications that require fast processing, such as mobile phones, SIFT does not meet the efficiency requirement due to its relatively slow computational speed. To address this problem, a new approach for LPL, which uses the oriented FAST and rotated BRIEF (ORB) feature detector, is proposed. The feature extraction in ORB is much more efficient than in SIFT and is invariant to scale and grayscale as well as rotation changes, and hence is able to provide superior performance for LPL. The potential regions of a license plate are detected by considering spatial and color information simultaneously, which is different from previous approaches. The experimental results on a challenging dataset demonstrate the effectiveness and efficiency of the proposed method.