In a conventional augmented reality system

the multi-scale image representations of a template image are constructed first. Feature key points at each scale are extracted and put together as a template feature set

which is used to match with the feature points extracted from the camera images. The number of feature points of the template image would become large when the number of scales in the template image representations is large. Nevertheless

camera images only correspond to images within a scale range similar to the scale of the camera image

and they probably overlapped with these images in partial regions. This result means that an ample amount of useless computation exists in conventional feature matching algorithms

thereby simultaneously lowering the image matching speed and decreasing registration accuracy. This paper proposes an effective method to locate image matching scales and regions in camera pose tracing and solve the preceding problem mentioned. Using local feature patching between current camera image features and the corresponding image scales and features of template image pyramid of regions achieves real-time computation of camera pose by feature matching pairs to solve feature matching accuracy and efficiency problem of the traditional three-dimensional tracing method. In the preprocessing stage

scale-space layers of a template image are constructed first. Concretely

an image is obtained by down-sampling the original image by a factor of 1.5

and it is sequenced as the second layer. On the condition that image resolution at the maximum layer is only less than that of the screen image specified

the other layers are formed by progressively half-sampling the original image and the second layer image and putting the two sequences alternately. Secondly

the key frame structure for each layer image is built. Specifically

each layer image is partitioned into the same rectangular regions

which could be overlapped when necessary. The size of the rectangular region is selected similar to that of the layer image at the maximum scale in scale-space layers. In each region

feature points are extracted and binary descriptors are generated by using the oriented FAST and rotated BRIEF algorithm

putting every rectangular position

sub-image

and feature points within it together to form a key frame structure. By this way

the feature descriptors of the image pyramid are managed according to scales and regions. In the real-time tracking stage

the scale range for any camera image within the image pyramid is located first. The covered image regions within this scale range are found using defined overlapping degree rules

thereby decreasing the scope of feature matching between current camera image features and template image pyramid and improving feature matching accuracy and efficiency by using local feature matching. 1) In locating scale range

a camera image

which is obtained in a distance to a template image

essentially corresponds to a scale range in the image pyramid of the template image and overlaps with some image regions in the scale range. This paper suggests a method for locating the scale range. First

this method predicts current camera pose in two ways: using the last frame camera pose and predicting the pose by Kalman filtering; four vertices of the original image are projected on the screen image with the evaluated camera pose; finally

the projection area size is obtained and used to compare with the layer image sizes in the image pyramid to determine the scale range. 2) In calculating the degree of region overlapping

we project all their key frame regions in layer images within the scale range onto the screen image with the evaluated camera pose to calculate the areas of the overlapped regions; the region overlapping degree is calculated through our method. 3) In local feature extraction and matching

a number of key frames with a large region overlapping degrees are obtained from the camera image by using the last frame camera pose as the evaluation; other key frames are obtained similarly by using pose evaluation from Kalman filtering. We consider the union of the two key frame sets and match all their feature points with those extracted from the camera image through the ORB algorithm and compute the camera pose by using some matching pairs. The new algorithm is implemented and run on a smartphone

tested on an open image database(Stanford mobile visual search dataset) with different resolution images and on other template images. This new algorithm is compared with four advanced algorithms

namely

fast locating of image scale and area

ORB

FREAK(fast retina keypoint)

and BRISK(binary robust invariant scalable keypoints). In experiments

videos are recorded and used for all testing template images

where camera translations

rotations

and scaling-related template images are included. The optimal parameters of the ORB

FREAK

and BRISK algorithms are selected by analysis and tests

and the registration error and running frame rates are tested before and after

respectively

integrating our feature matching algorithm with the optical flow algorithm. Experimental results show that our new algorithm is robust and has high registration accuracy with approximately one pixel and has a real-time 3D tracking rate of 20-30 frames per second. The algorithm can locate an image scale and region much better than before. The feature matching accuracy and speed between the current camera and template images increase obviously compared with several classic algorithms

especially when the resolution of the image is high. This algorithm can be used to track the natural image on a mobile platform.