OGB: A Distinctive and Efficient Feature for Mobile Augmented Reality

The distinctiveness and efficiency of a feature descriptor used for object recognition and tracking are fundamental to the user experience of a mobile augmented reality (MAR) system. However, existing descriptors are either too compute-expensive to achieve real-time performance on a mobile device, or not sufficiently distinctive to identify correct matches from a large database. As a result, current MAR systems are still limited in both functionalities and capabilities, which greatly restrict their deployment in practice. In this paper, we propose a highly distinctive and efficient binary descriptor, called Oriented Gradients Binary (OGB). OGB captures the major edge/gradient structure that is an important characteristic of local shapes and appearance. Specifically, OGB computes the distribution of major edge/gradient directions within an image patch. To achieve high efficiency, aggressive down-sampling is applied to the patch to significantly reduce the computational complexity, while maintaining major edge/gradient directions within the patch. Comparing to the state-of-the-art binary descriptors including ORB, BRISK and FREAK, which are primarily designed for speed, OGB has similar construction efficiency, while achieves a superior performance for both object recognition and tracking tasks running on a mobile handheld device.