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.
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