快速定位图像尺度和区域的3维跟踪算法

Three dimensional tracking with fast locating of image scale and area

目的传统的2维自然图像的增强现实算法,对模板图像的各个尺度下的整个图像提取特征点并保存到特征点数组中,跟踪阶段对模板图像提取出的所有特征点进行匹配,造成了大量的无效运算,降低了特征匹配的效率。为了解决这个问题,将模板各个尺度的图像进行区域划分,提出了一种快速定位图像尺度和区域的算法,缩小特征匹配的范围,加快3维跟踪的速度。方法预处理阶段,通过对图像金字塔每一尺度图像分成小区域,对模板图像上的特征点进行分层次分区域的管理。在系统实时跟踪阶段,通过计算几何算法快速确定当前摄像机图像所对应的尺度和区域,从而减小了特征匹配的范围。结果该方法大幅度缩小了特征匹配的范围,降低了特征匹配所消耗的时间,与传统算法相比,在模板图像分辨率较大的情况下特征匹配阶段时间可以缩短10倍左右,跟踪一帧图像的时间缩短1.82倍。系统实时跟踪过程中的帧率总体保持在15帧/s左右。结论提出的快速定位图像尺度和区域算法适用于移动设备上对2维自然图像的跟踪,尤其在模板图像分辨率较大的情况下,算法能够显著减小特征匹配的范围,从而提升了实时3维跟踪算法的运行效率。

Objective The traditional augmented reality algorithms for natural image detect and extract the natural features of a template image for multi scales. At the tracking stage the algorithms match the screen features with all of the features ex- tracted from the template image, which produces a large amount of useless computation and reduces the efficiency of feature matching. To solve this problem, this article presents an algorithm which can locate the scale and area of the template im- age fast and accelerate the speed of 3D tracking. Method In the preprocess stage, the image in each scale is partitioned in- to small areas, and the features of the template image are managed according to the scale and area. In the real-time tracking stage, the method locates the scale and area of current camera image quickly using some computation geometry algorithms so that the scope of feature matching is decreased. Result The search field of feature matching, cuts down the time spent by feature matching, compared with the traditional method, the time spent by feature matching is cut short by about 10 times and the time spent by tracking one frame is cut short by 1.82 times when the resolution of template image is high. Theframe rate of the 3d tracking algorithm keeps at 15 frames per second on average. Conclusion Our method can be used to the tracking of natural image on mobile platform, especially when the resolution of image is high, the method can narrow the search field of image scale and area and promote the efficiency of real-time 3d tracking algorithm.