针对增强现实系统在复杂环境中跟踪注册稳健性不足以及特征搜索空间大的问题,提出一种结合显著性检测的混合跟踪注册方法。首先,利用均值漂移迭代运算预测候选目标位置,建立峰值位于目标中心的二维高斯函数,生成融合中心先验的视觉显著图;然后,提取目标显著性特征,采用均值漂移算法进行跟踪;另外,根据相似度量系数判断是否利用形变多样相似性匹配算法重定位目标;最后,构建多尺度空间的快速定向二进制描述算法进行局部目标区域的特征检测,来匹配计算得到的注册参数,完成虚实融合。实验结果表明,本方法能有效解决目标跟踪算法在背景杂波、目标遮挡、目标丢失情况下跟踪不稳定及目标检测准确度不高等问题,使增强现实系统更具稳定性。

This study proposes a hybrid tracking registration method based on saliency detection for solving the insufficient robustness of tracking registration in a complex environment and the huge feature-searching space of the augmented reality system. Based on this method, the mean-shift iteration is initially employed to predict the candidate target position. Subsequently, a two-dimensional Gaussian function is constructed with a peak at the target center, and a visual saliency map of the fusion center prior is generated. Next, the target salient feature is extracted, and the mean-shift algorithm is applied to tracking. Furthermore, the similarity measurement coefficient is used to determine whether to utilize the deformation diversity similarity-matching algorithm for relocating the target. Finally, we construct a multiscale-space fast directional binary description algorithm that performs the feature detection and matching calculation with respect to the local target area to obtain the registration parameters, and the virtual-real fusion is completed. The experimental results demonstrate that the proposed method effectively solves the problems of tracking instability and low accuracy of target detection by using the target-tracking algorithm in the cases of background clutter, target occlusion, and target loss, improving the stability and robustness of the augmented reality system.