Abstract: To achieve fast feature matching between current camera image features and the regions’features of the template image pyramid within the corresponding image scales so as to solve matching accuracy and efficiency of image tracking,an effective method to track a template image by managing its feature points in sub-images within scale-space layers was proposed.In the preprocessing stage,scale-space layers of a template image were constructed,and every scale-space layer image was partitioned into rectangular regions.Then,a key frame structure for each region’s image was built.Specifically,ORB(oriented FAST and rotated BRIEF)key points and Harris key points were extracted from each region’s image,and a BoW(bag of words)feature vector was built from its ORB feature descriptors.For each region,its position,BoW feature vector and Harris key points consist of a key frame structure.In the real-time tracking stage,three tracking branches,i.e.predicting tracking,relocalisation tracking and optical flow tracking,were pointed and processed respectively.In predicting tracking and relocalisation tracking,matching scales and regions of the camera image were fast located by using key frame information,then local feature matching between cam-era image features and the features in its matching scales and regions was done,and camera pose was solved by the feature matching pairs.In optical flow tracking,a method to renew tracking feature points by using Harris points in key frames was proposed to raise the running frame numbers.The new algorithm was compared with five advanced algorithms,i.e.FLISA,IFLISA,ORB,FREAK and BRISK,tested on mobile device using an open image database(Stanford mobile visual search dataset)with different resolution images.The experimental results show that our new algorithm is robust,has higher registration accuracy with less than one pixel,and achieves a real-time camera pose tracking rate of 20-30 frames per second.