With the development of mobile Internet

the speed of advancement of mobile augmented reality applications is accelerating. However

given that storage and computation capacity are limited on mobile devices and that large-scale outdoor images mostly include similar building structure images

these applications are restricted to indoor environments. To solve this problem

a mobile augmented reality system based on cloud image recognition is established. To reduce mistake-matching probability

we add gravity information into the SURF and BRISK feature description algorithm. Gravity information is used as the main orientation. In our system

these two description algorithms are named Gravity-SURF and Gravity-BRISK. In the cloud

large-scale images and augmented information are managed by the augmented reality management system. The VLAD algorithm based on Gravity-SURF is applied to large-scale image recognition. On intelligent terminals

different user interfaces are designed for augmented reality applications to display augmented information. The model rendered is a presentation format of the augmented information. We propose a method that combines Gravity-BRISK and optical flow to perform a real-time camera tracking algorithm. When using rendering engine Unity3D for 3D rendering

we propose a solution for the transformation of camera pose in different coordinate systems. Thereafter

the camera pose data will be transmitted from an Android platform to Unity3D by the Android Native Development Kit. In the experiments

we first build an image database with 4 000 images. The database contains 800 outdoor scenes and each scene contains 5 images in different angles and distances. Each image must be associated with the local gravity information. Thereafter

we compare the traditional description algorithm with the gravity-based description algorithm. From the experiments

we can conclude that the gravity-based description algorithm is able to enhance the discrimination of similar features and improve the matching accuracy. Finally

we test the performance and accuracy of our image recognition algorithm and tracking algorithm. The recognition algorithm experiments show that the VLAD algorithm based on Gravity-SURF can improve the recognition rate. In 4G and WIFI network environments

the upload time of the recognition image is less than 40 ms

the online recognition time is approximately 420 ms

and the augmented data download time is less than 4 s. Furthermore

the tracking algorithm experiments show that the RMS errors in variation of the rotation and scale are all less than 1.2 pixels. For the optical flow stage

the frame rate is able to reach 23 frame/s. In terms of the above experiments

the algorithm can satisfy the accuracy and real-time demands of the system. The mobile augmented reality system is suitable for complex outdoor environments. This system has been applied in Google Glass and the journalism field but is not limited to these two areas. Nonetheless

the augmented information in different applications can be managed efficiently. The system we established aims to promote the development of MAR applications.